In recent years, the rise of virtual work opened up a new way of responding to the city’s brain drain. Five years after the George Kaiser Family Foundation began offering $10,000 to remote workers willing to move to Tulsa for at least a year, some 3,300 people have taken up the offer.

Can Remote Workers Reverse Brain Drain? - The New York Times

Because these voices are processed like any other sound in the brains of people who experience auditory hallucinations, the team at NYU Shanghai's Institute of Brain and Cognitive Science decided to put electroencephalogram (EEG) monitors on people with schizophrenia — 20 participants who ...

Scientists Say They’ve Traced Back the Voices Heard by People With Schizophrenia

Scientists have mapped out how 140,000 neurons are wired in the brain of the fruit fly, Drosophila melanogaster.

After a Decade, Scientists Unveil Fly Brain in Stunning Detail - The New York Times

Let's go ahead and dive in. Andrew, you've been a global leader in both AI and education for decades. When you first started Google Brain in 2011, were you thinking about the impact AI might have on the workforce and the economy of the future?

Transcript: The Futurist: Training for the AI Age - The Washington Post

Science/AAAS peer-reviewed journals deliver impactful research, daily news, expert commentary, and career resources.

Brain fluid may travel to distant parts of body

Florida scientists use AI and virtual reality to create 3D renderings of brain formations of mice, whose neuron types are like humans’

AI brain map could help demystify Alzheimer’s and autism | Neuroscience | The Guardian

Ancient Egyptians might have tried to treat brain cancer: what a new study found

प्राचीन इजिप्शियन लोकांनी मेंदूच्या कर्करोगावर शोधला होता उपचार; नवीन संशोधन नेमके काय सांगते?

A recent study suggests that the rate of aging in our brains is connected to the nutrients we consume in our diets.

Unlocking Longevity: A Specific Diet Linked To Slower Brain Aging, Study Finds

Brain

brain

"Our Intelligence is what makes us human and AI is an extension of that Quality" -Yan LeCun Since the advent of (also known as artificial neural networks), the AI industry has enjoyed unparalleled success. Neural Networks are the driving force behind modern AI systems and they are modeled after the human brain. Modern AI research involves creating and implementing algorithms that aim to mimic the neural processes of the human brain to create systems that learn and act in ways similar to human beings. Neural Networks In this article, we will attempt to understand the brain-inspired approach to building It is worth noting that while this topic is an inherently broad one, I will be as brief and succinct as possible. I plan to treat sub-topics that have more intricate sub-branches as standalone articles, and I will, of course, leave references at the end of the article. AI systems. Content breakdown How I hope to approach this: Here we'll discuss how scientists Norman Weiner ann Warren McCulloch brought about the convergence of neuroscience and computer science, how Frank Rosenblatt's Perceptron was the first real attempt to mimic human intelligence, and how its failure brought about ground-breaking work which would serve as the platform for Neural Networks. History of the brain-inspired approach to AI: In this section, we'll dive into the biological basis for the brain-inspired approach to AI. We will discuss the basic structure and functions of the human brain, understand its core building block, the neuron, and how they work together to process information and enable complex actions. How the human brain works and how it relates to AI systems: Here we will discuss the fundamental concepts behind the brain-inspired approach to AI. We will explain how concepts such as; Neural networks, Hierarchical processing, and plasticity and how techniques of parallel processing, distributed representations, and recurrent feedback aid AI in mimicking the brain's functioning. The Core Principles behind the Brain-inspired Approach to AI: Here we will talk about the challenges and limitations inherent in attempting to build systems that mimic the human brain. Challenges such as; the complexity of the brain, and the lack of a unified theory of cognition explore the ways these challenges and limitations are being addressed. Challenges in building AI systems modeled after the human brain: Let us begin! The History of the brain-inspired Approach to AI The drive to build machines that are capable of intelligent behavior owes much of its inspiration to MIT Profesor, . Norbert Weiner was a child prodigy who could read by the age of three. He had a broad knowledge of various fields such as Mathematics, Neurophysiology, medicine, and physics. Norbert Weiner Norbert Weiner believed that the main opportunities in science lay in exploring what he termed as -areas of study that are not clearly within a certain discipline but rather a mixture of disciplines like the study of medicine and engineering coming together to create the field of Medical Engineering-, he was quoted saying: Boundary Regions "If the difficulty of a physiological problem is mathematical in nature, ten physiologists ignorant of mathematics will get precisely as far as one physiologist ignorant of mathematics" In the year 1934, Weiner and a couple of other academics gathered monthly to discuss papers involving boundary region science. It was a perfect catharsis for half-baked ideas, insufficient self-criticism, exaggerated self-confidence and pomposity - Norman Weiner From these sessions and from his own personal research, Weiner learned about new research on biological nervous systems as well as about pioneering work on electronic computers, and his natural inclination was to blend these two fields. And so, a relationship between neuroscience and computer science was formed. This relationship became the cornerstone for the creation of artificial intelligence, as we know it. After World War II, Wiener began forming theories about intelligence in both humans and machines and this new field was named . Wiener’s foray into Cybernetics successfully got scientists talking about the possibility of biology fusing with engineering. One of the said scientists was a neurophysiologist named , who then proceeded to drop out of Haverford University to study philosophy and psychology at Yale. Cybernetics Warren McCulloch While attending a scientific conference in New York, he came in contact with papers written by colleagues on biological feedback mechanisms. The following year, in collaboration with his brilliant 18-year-old protege named Walter Pitts, McCulloch proposed a theory about how the brain works -a theory that would help foster the widespread perception that computers and brains function essentially in the same way. They based their conclusions on research by McCulloch on the possibility of neurons processing Binary Numbers (for the unknowledgeable, computers communicate via binary numbers). This theory became the foundation for what became the first model of an artificial neural network, which was named the McCulloch-Pitts Neuron(MCP). The MCP served as the foundation for the creation of the first-ever neural network which came to be known as . The Perceptron was created by Psychologist, who, inspired by the synapses in the brain, decided that as the human brain could process and classify information through synapses(communication between neurons) then perhaps a digital computer could do the same via a neural network. the perceptron Frank Rosenblatt The Perceptron essentially scaled the MCP neuron from one artificial neuron into a network of neurons, but, unfortunately, the perceptron had some technical challenges which limited its practical application, most notable of its limitations was its inability to perform complex operations(like classifying between more than one item, for example, a perceptron could not perform classification between a cat, a dog, and a bird). In the year 1969, a book published by and titled lay out in detail the flaws of the Perceptron and due to that, research on Artificial Neural Networks stagnated until the proposal of Back Propagation by . Marvin Minsky Seymour Papert Perceptron Paul Werbos Back Propagation hopes to solve the issue of classifying complex data that hindered the industrial application of Neural Networks at the time. It was inspired by synaptic plasticity; the way the brain modifies the strengths of connections between neurons and as such improves performance. Back Propagation was designed to mimic the process in the brain that strengthens connections between neurons via a process called weight adjustment. Despite the early proposal by Paul Werbos, the concept of backpropagation only gained widespread adoption when researchers such as , , and published papers that demonstrated the effectiveness of backpropagation for training neural networks. The implementation of backpropagation led to the creation of Deep Learning which powers most of the AI systems available in the world. David Rumelheart Geoffrey Hinton Ronald Williams "People are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at." - Parallel Distributed Processing How the human brain works and how it relates to AI systems We have discussed how researchers began to model AI to mimic the human brain, let us now look at how the brain works and define the relationship between the brain and AI systems. How the brain works: A simplified description The human brain, essentially processes thoughts via the use of neurons, a neuron is made up of 3 core sections; The Dendrite, Axon, and the Soma. The Dendrite is responsible for receiving signals from other Neurons, The Soma processes information received from the Dendrite, and the Axon is responsible for transferring the processed information to the next Dendrite in the sequence. To grasp how the brain processes thought, imagine you see a car coming towards you, your eyes immediately send electrical signals to your brain through the optical nerve and then the brain forms a chain of neurons to make sense of the incoming signal. The first neuron in the chain collects the signal through its and sends it to the to process the signal after the Soma finishes with its task it sends the signal to the which then sends it to the Dendrite of the next neuron in the chain, the connection between Axons and Dendrites when passing on information is called a Synapse. The entire process continues until the brain finds a t(that's scientific lingo for; the brain continues processing until it finds an optimal response to the signal sent to it) and then it sends signals to the necessary effectors eg, your legs and then the brain sends a signal to your legs to run away from the oncoming car. Dendrites Soma Axon Sapiotemporal Synaptic Inpu The relationship between the brain and AI systems The relationship between the brain and AI is largely mutually beneficial with the brain being the main source of inspiration behind the design of AI systems and advances in AI leading to a better understanding of the brain and how it works. There is a reciprocal exchange of knowledge and ideas when it comes to the brain and AI, and there are several examples that attest to the positively symbiotic nature of this relationship: Arguably the most significant impact made by the human brain to the field of Artificial Intelligence is the creation of Neural Networks. In essence, Neural Networks are computational models that mimic the function and structure of biological neurons, the architecture of neural networks and their learning algorithms are largely inspired by the way neurons in the brain interact and adapt. Neural Networks: AI systems have been used to the human brain and study its interactions with the physical world. For example, researchers have Machine Learning techniques to simulate the activity of biological neurons involved in visual processing, and the result has provided insight into how the brain handles visual information. Brain Simulations: simulate Researchers have begun using Machine Learning Algorithms to analyze and gain insights from brain data, and fMRI scans. These insights serve to identify patterns and relationships which would otherwise have remained hidden. The insights gotten can help in the understanding of internal cognitive functions, memory, and decision-making, it also aids in the treatment of brain-native illnesses such as Alzheimer's. Insights into the brain: Core Principles Behind the brain-inspired Approach to AI Here we will discuss several concepts which aid AI in imitating the way the human brain functions. These concepts have helped AI researchers create more powerful and intelligent systems which are capable of performing complex tasks. Neural Networks As discussed earlier, neural networks are arguably the most significant impact made by the human brain to the field of Artificial Intelligence. In essence, Neural Networks are computational models that mimic the function and structure of biological neurons, the networks are made up of various layers of interconnected nodes, called artificial neurons, which aid in the processing and transmitting of information, similar to what is done by dendrites, somas, and axons in biological neural networks. Neural Networks are architected to learn from past experiences the same way the brain does. Distributed Representations Distributed representations are simply a way of encoding concepts or ideas in a neural network as a pattern along several nodes in a network in order to form a pattern. For example, the concept of smoking could be represented(encoded) using a certain set of nodes in a neural network and so if that network comes along an image of a man smoking it then uses those selected nodes to make sense of the image (it's a lot more complex than that but for the sake of simplicity), this technique aids AI systems in remembering complex concepts or relationships between concepts the same way the brain recognizes and remembers complex stimuli. Recurrent Feedback This is a technique used in training AI models where the output of a neural network is returned as input to allow the network to integrate its output as extra data input in training. This is similar to how the brain makes use of feedback loops in order to adjust its model based on previous experiences. Parallel Processing Parallel processing involves breaking up complex computational tasks into smaller bits in an effort to process the smaller bits on another processor in an attempt to improve speed. This approach enables AI systems to process more input data faster, similar to how the brain is able to perform different tasks at the same time(multi-tasking). Attention Mechanisms This is a technique used which enables AI models to focus on specific parts of input data, it is commonly employed in sectors such as Natural Language Processing which contains complex and cumbersome data. It is inspired by the brain's ability to attend to only specific parts of a largely distracting environment; like your ability to tune into and interact in one conversation out of a cacophony of conversations. Reinforcement Learning Reinforcement Learning is a technique used to train AI systems, it was inspired by how human beings learn skills through trial and error. It involves an AI agent receiving rewards or punishments based on its actions, this enables the agent to learn from its mistakes and be more efficient in its future actions (this technique is usually used in the creation of games). Unsupervised Learning The brain is constantly receiving new streams of data in the form of sounds, visual content, sensory feelings to the skin, etc and it has to make sense of it all and attempt to form a coherent and logical understanding of how all these seemingly disparate events affect its physical state. Take this analogy as an example, you feel water drop on your skin, you hear the sound of water droplets dropping quickly on rooftops, you feel your clothes getting heavy and in that instant, you know rain is falling, you then search your memory bank to ascertain if you carried an umbrella and if you did, you are fine, else, you check to see the distance from your current location to your home if it is close, you are fine, else you try to gauge how intense the rain is going to become if it is a light drizzle you can attempt to continue the journey back to your home, but if it is priming to become a shower, then you have to find shelter. The ability to make sense of seemingly disparate data points(water, sound, feeling, distance) is implemented in Artificial intelligence in the form of a technique called Unsupervised Learning. It is an AI training technique where AI systems are taught to make sense of raw, unstructured data without explicit labeling ( no one tells you rain is falling when it is falling, do they/). Challenges in Building Brain-Inspired AI Systems We have spoken about how the approach for using the brain as inspiration for AI systems came about, how the brain relates to AI, and the core principles behind brain-inspired AI. In this section, we are going to talk about some of the technical and conceptual challenges inherent in building AI systems modeled after the human brain. Complexity This is a pretty daunting challenge. The brain-inspired approach to AI is based on modeling the brain and building AI systems after that model, but the human brain is an inherently complex system with 100 billion neurons and approximately 600 trillion synaptic connections(each neuron, has on average, 10,000 synaptic connections with other neurons), and these synapses are constantly interacting in dynamic and unpredictable ways. Building AI systems that are aimed to mimic, and hopefully exceed, that complexity is in itself a challenge and requires equally complex statistical models. Data Requirements for Training Large Models Open AI's GPT 4, which is, at the moment, the cutting edge of text-based AI models requires 47 GigaBytes of data, in comparison, its predecessor GPT3 was trained on 17 Gigabytes of data, which is approximately 3 orders of magnitude lower, imagine how much GPT 5 will be trained on. As has been proven in order to get acceptible results, Brain-Inspired AI systems require vast amounts of data and data for tasks especially auditory and visual tasks and this places a lot of emphasis on the creation of data collection pipelines, for instance, Tesla has 780 million miles of driving data and its data collection pipeline adds another million every 10 hours. Energy Efficiency Building brain-inspired AI systems that emulate the brain's energy efficiency is a huge challenge. the human brain consumes approximately 20 watts of power, in comparison, 's Autopilot, on specialized chips, consumes about 2,500 watts per second and 7.5-megawatt hours(MWh) to train an AI model the size of . Tesla it takes around ChatGPT The Explainaibility Problem Developing brain-inspired AI systems that can be trusted by users is crucial to the growth and adoption of AI, but therein lies the problem, the brain, which AI systems are meant to be modeled after, is essentially a black box. The inner workings of the brain are not easy to understand, this is due to the lack of information surrounding how the brain processes thought. There is no lack of research on how the biological structure of the human brain but there is a certain lack of empirical information on the functional qualities of the brain, that is, how thought is formed, how deja vu occurs, etc, and this leads to a problem in the building of brain-inspired AI systems. The Interdisciplinary Requirements The act of building brain-inspired AI systems requires the knowledge of experts in different fields, such as; Neuroscience, Computer Science, Engineering, Philosophy, and Psychology. But there lies a challenge there both logistical and foundational, in the sense that getting experts from different fields very financially tasking, and also there lies the problem of knowledge conflict; it is really difficult to get an engineer to care about the psychological effects of what he/she is building, not to talk of the problem of colliding egos. Summary In Conclusion, while the brain-inspired approach is the obvious route to building AI systems(we have discussed why), it is wrought with challenges but we can look to the future with the hope that efforts are being made to solve these problems. Also published here. References FreeCode Camp Machine Learning Tesla's Vehicle Safety Report Basic Neural Units of the Brain: Neurons, Synapses and Action Potential When Brain-inspired AI meets AGI Perceptron: The artificial Neuron (An Essential Upgrade To The McCulloch-Pitts Neuron) McCulloch-Pitts Neuron — Mankind’s First Mathematical Model Of A Biological Neuron BackPropagation through Time: What it does and How to do it The History of AI BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework

The Cognitive Paradigm: Exploring Brain-Inspired AI Development 

জ্ঞানীয় দৃষ্টান্ত: মস্তিষ্ক-অনুপ্রাণিত এআই বিকাশের অন্বেষণ

Das kognitive Paradigma: Erforschung der vom Gehirn inspirierten KI-Entwicklung

El paradigma cognitivo: exploración del desarrollo de IA inspirado en el cerebro

Le paradigme cognitif : explorer le développement de l'IA inspirée par le cerveau

संज्ञानात्मक प्रतिमान: मस्तिष्क-प्रेरित एआई विकास की खोज

認知パラダイム: 脳にヒントを得た AI 開発の探求

인지 패러다임: 뇌에서 영감을 받은 AI 개발 탐색

O Paradigma Cognitivo: Explorando o Desenvolvimento de IA Inspirado no Cérebro

Bilişsel Paradigma: Beyinden Esinlenen Yapay Zeka Gelişimini Keşfetmek

Mô hình nhận thức: Khám phá sự phát triển AI lấy cảm hứng từ não bộ

认知范式：探索受大脑启发的 AI 开发

"We’re designing the first neural chip implant that will let you control a computer or mobile device anywhere you go." -Neuralink We've all probably watched movies like , and __ where there's always a crazy smart guy who creates a smart computer and then eventually can't control it anymore. Then the robot sees humans and decides the best way to solve the problems humans pose would be to kill every human on earth (like that will ever work). Matrix Terminator I Robot But luckily there comes some ordinary guy whose life is suckish at the beginning of the movie but then suddenly the ordinary guy 'amazingly' gets to know that computers want to kill humans and he has the responsibility to save the world (sometimes he gets powers) and so with some trepidation he goes after the robots with a suckish plan. But he almost fails because of the suckish plan or whatever and the robots win but then the hero discovers his inner strength and then can save the world and he gets to kiss the hot girl (lucky dude) he would never have been able to kiss if he was still the ordinary guy. The movie ends with the guy becoming more sure of himself and well his life changes (and all this happens within 2hrs, cool huh). Well, these days the stuff we only thought happened in movies are happening in real life from self-driving cars, to Siri, Google Assistant and generally AI, and well let's be honest we're all scared that our devices are going to come after us someday the same way they do in the movies (I mean after terminator who wouldn't). But I'm hoping that after reading all this, you'll know your technology will not kill you and I'll be doing that with something called BMIs...... robot dogs But First WHAT IS A BMI? BMI is an acronym that stands for Brain-Machine Interface also Known as Brain-Computer Interface(BCI). According to Brain-Machine Interface(BMI) "Is a direct communication pathway between an enhanced or wired brain and an external device". In simple words, BMIs are simply a machine's ability to talk to and understand commands from the human brain due to a connection between the two of them. In other words, it is simply the ability to talk to your computer using your brain...... , Wikipedia Now we are going to talk about a very popular BMI company Neuralink, what they do, how they do what they do and why. But first What is Neuralink? Neuralink is an American BMI company- that means they create Brain-Machine Interface systems. The company was made popular because of its association with (Yup the billionaire, Elon Musk). According to the company's Neuralink is a team of exceptionally talented people who are creating the future of brain Interfaces... Elon Musk , Website HOW THE BRAIN WORKS The brain is made up of nerve cells called neurons which are responsible for processing thought. There are many types of neurons but all neurons generally have 3 parts: Dendrites, Soma and Axon. The dendrite is responsible for receiving electrical signals. The Soma is responsible for computing / Processing electrical signals. The Axon is responsible for passing out computed electrical signals to the next Dendrite in the chain. The brain communicates information through the use of electrical signals. The brain is made up of 86 billion neurons, and when processing a particular event (Action Potential) it uses a chain of neurons to process events. For instance, when you see a car coming towards you your eyes immediately send electrical signals to your brain through the optical nerve, and then the brain forms a chain of neurons to make sense of the incoming signal and so the first neuron in the chain collects the signal through its Dendrites and sends it to the Soma to process the signal. After the Soma finishes with its task it sends the signal to the Axon which then sends it to the Dendrite of the next neurone in the chain, the connection between Axons and Dendrites when passing on information is called a Synapse. So the entire process continues until the brain finds a Sapiotemporal Synaptic Input(that's scientific Lingo for the brain continues processing until it finds an optimal response to the signal sent to it) and then it sends signals to the necessary effectors eg, you're legs and then the brain sends a signal to your legs to run away from the oncoming car. Why Does Neuralink Want to Put A Chip in my Head? Now you must understand that for Neuralink's BMI to work they don't actually need to put the chip into your head. But it would make the BMI technology more effective, putting a chip outside the head is like watching a football game outside the stadium. You'd hear the noises from those inside the stadium and know when something good happens but you wouldn't be able to know exactly how a player scored or missed. So putting a chip into the human head is really for no other reason apart from the clarity it gives. Because for the chip to be able to efficiently decode the electrical signals being used by the brain to communicate and then send to the computer it has to be able to receive the exact waves being generated by the brain and this cannot be achieved efficiently when the chip is placed outside the brain. How Neuralink Puts the Chip Into our Brain The electrode threads in the Neuralink chip (AKA N1 chip) are extremely small (roughly 23 millimeters in diameter). So it is almost impossible for it to be surgically placed into the brain by human hands. The guys at Neuralink (remember they are brilliant) built an automated precision robot (picture is above) that surgically places the chip into the human brain. Now you might be wondering how many chips you need to have in your brain, a single neural link chip can cover up to 1000 brain cells and we have billions of brain cells so you do the math. How the Chip in Your Head Allows You to communicate with your Computer The Neuralink chip is placed into the parts of the brain that monitor movement. The chip contains tiny fibre threads called neural threads and each small thread contains many electrodes. The electrodes are responsible for receiving electrical signals from the brain and then sending them to the chip. The chip then turns the electrical signals it receives into the binary that the computer can understand. For instance, if you have a neuralink chip implanted into you and you want to move your computer mouse cursor all you have to do is think about moving your arm and then your brain sends electrical signals to your hands and in the process. The fibre threads in the N1 chip receive those signals using the electrodes and the chip then turns those electrical signals into binary and then sends the binary to your computer using a wireless connection and your computer mouse cursor. The same thing happens when you want to charge your chip, the charger is compact inductive (That just means it can wirelessly detect your chip while it's in your head! ) and so it wirelessly connects to your chip and charges in from the outside (cool huh). How does Having a Chip in our Brains Help Humanity? The N1 chip will help humanity in a lot of ways. Picture for a moment that crippled man on the street, that deaf person or that blind guy who can't join WhatsApp, Instagram, Twitter or play video games simply because they can't operate a computer. Well to put it simply Neuralink gives them that opportunity. It also helps healthy human beings by making it possible for us to access our technology more easily. How Safe is the Technology? From the beginning of time, man has misused technology from guns, ships to planes, and cars but that hasn't stopped us from using technology has it some people use guns to defend themselves. While some use guns to rob others, others use them to defend the innocent. Some people use airplanes to travel but others use them to commit terrorism; some people use smartphones to connect with friends and family and some people use them to plan criminal activities. , it is the people who use them to do bad things that are evil. The main point is: technology isn’t inherently evil And let me ask you: what is the difference between Neuralink today and the smartphone to your great grandfather? Technology has always improved and people have always abused it but it is the people and not the technology that is evil, and it is our responsibility as humans to fight against those who want to limit technology. Don't we owe it to future generations to do this? After all: who are we to stand in the way of the future?

Should You Let Them Put a Chip in Your Brain?

Why performance marketing isn’t enough I recently spoke to a really smart group of performance marketers about the importance of brand marketing. This was a very analytical group, so I decided to attempt to reduce the fluffy reputation of brand marketing by talking about neuroscience and how the brain works when it comes to decision making. I started by showing the group a slide with the following logo pairings on them. As we went through each pairing one by one, I asked them to , and I’d like you to do this too. Quickly. Don’t think about why, just choose one for each. choose one (No more comments on this for now — we’ll come back to this later) tells us that consumers are rational agents who make choices after considering all the available information, costs, benefits and event probabilities, using the rational part of the brain (the Neocortex). Rational choice theory Sure. Your Starbucks coffee, trip to Bali, Chanel bag — all very rational right? Of course not, we all know this. Every one of us can think of times when we have bought items we didn’t choose rationally, and this happens every day. While some consumers are more disciplined than others with their planning, even the most rational of us do this all the time. Where it gets interesting is understanding (last minute or otherwise). When I grab the ice cream at the grocery store, why do I choose Ben and Jerry’s over Haagen Dasz? I might tell myself it’s the specific flavour or the price or the packaging, or post-rationalise it in my brain in one of a million other ways. But for some reason, I instantly go for one over the other. This is . Brand salience is that trigger in your brain that, when the moment comes to buy, says, choose that one. why we decide to choose our chosen brands at the point of purchase brand salience also known as , is the newer field that believes . According to Harvard Business School professor : Neuromarketing , consumer neuroscience consumer decisions are actually made in the older or more unconscious parts of the brain Uma R. Karmarkar “People are fairly good at expressing what they want, what they like, or even how much they will pay for an item. But , or how and when it is influenced by factors like store displays or brands. [Neuroscience] can help us understand those hidden elements of the decision process.” they aren’t very good at accessing where that value comes from Big caveat: I am not a neuroscientist. While many neuroscientists believe in this, some don’t! You’ll need to make your own mind and decide for yourself. In order for any of this to make sense, you’ll need a quick overview of the brain. This is based on neuroscientist Paul MacLean’s . Triune Brain Theory According to this theory, the brain is comprised of 3 parts: the Reptilian Brain, the Limbic Brain, and the Neocortex. The Reptilian Brain Credit: Conscious Discipline Brain State Model The Reptilian Brain is also known as the Old Brain, the Lizard Brain, or the R-Complex. As you can probably guess, this is the oldest part of our brain and goes back a long time! It first appeared in fish around . 500 million years ago The Reptilian Brain includes the brainstem and cerebellum and it controls the body’s core functions like heart rate and breathing. It drives like survival instinct, direct-stimulus response, fight-or-flight, competition and aggression. It gave our ancestors the skills they needed to survive. Basically, this part of your brain is all about impulse and instinct since these were critical for survival. primal behaviours The checkout at the grocery store has thrived based on instinct and impulse buys. Ecommerce retailers now replicate this online at the end of your purchase journey. It works very well for low-ticket items, but there are also many ways of experimenting with calls to action for higher ticket items that appeal to the same part of the brain. It’s the reason why the real estate agent tells you there’s another buyer who is very interested too. Or the online retailer that tells you there’s only one item left so you should buy it before it’s gone. All of these tactics that push urgency appeal to our Reptilian Brains. The Limbic Brain Credit: Conscious Discipline Brain State Model Also known as the Limbic System or the Mammal Brain, this part of the brain evolved next, after the Reptilian Brain. Of course “next” in this context was significantly later in our biological evolution by around . 250 million years This part of the brain includes the hippocampus, amygdala and hypothalamus. This is where come from. If the characters from really were living inside your head, their headquarters would firmly be planted in the Limbic Brain. emotions Inside Out Credit: Disney/Pixar This part of your brain is responsible for feelings, memories, value judgments, trust, loyalty, hopes and dreams. Your Limbic Brain a romcom. It generates and regulates the flow of chemicals and chemical reactions that create emotions. That you get when you buy a lottery ticket and think about what you’d do if you win? That’s got the Limbic Brain written all over it. loves dopamine shot The Limbic Brain has no capacity for language. So if you post-rationalise an emotive decision made ( ) you’re doing it in your Neocortex, which we’re about to discuss. With no language capacity in the Limbic Brain (or the Reptilian Brain for the record), you’re making decisions based on gut feelings, not rational logic. The Limbic Brain is the part of you that drives you to innovate and try new things. It’s not actually logical to do either, but this part of your brain understands that the upside of how it could make you feel makes it worth it. well actually it was the right thing to do because… The Limbic Brain is the reason you buy a plane ticket to an exotic destination on the other side of the world. It’s why you pay for skydiving in Interlaken and scuba diving in Great Barrier Reef. It’s why you buy the really beautiful but expensive trench coat from the luxury brand instead of the cheap one from the mall. It’s why you leave your stable job to go back to school for a career change. That’s me in blue the moment before I jumped out of a plane in Interlaken / Credit: My Limbic Brain None of these are rational decisions, but you do them because with high risk comes high reward. Your Limbic Brain has taught you this. The Neocortex Credit: Conscious Discipline Brain State Model The Neocortex is also called the Cerebral Cortex, the Human Brain or the New Brain. It was the to evolve. The Neocortex performs like cognition, sensory perception, language, abstract thought, imagination and consciousness. This includes logic, reason, science, art, music and creativity. last part of the brain higher-order brain functions Unlike the older parts of the brain, the Neocortex likes to compare and contrast and think metaphorically. It’s split into two hemispheres: Left brain: analytical, logical Right brain: creative, intuitive The Neocortex uses tangible factors and language to rationalise what we can’t rationalise in the other parts of the brain. So if I choose Ben and Jerry’s ice cream, my Neocortex might tell me that it’s because I align with their values or because Chocolate Chip Cookie Dough just tastes so good. But the decision was already made, this rationalisation has come after. The same goes for the exercise you did with the various brand logos at the beginning of this article. Something in your gut made you choose one over the other — the Neocortex just made it make sense to you. As crazy as it may seem, the (which is the rational, reasonable part of your brain, a part of the Neocortex) is responsible for only 5% of the decision making process. Performance marketing lives here. In reality, the vast majority of consumer decision making comes from the Limbic Brain and Reptilian Brains. prefrontal cortex “We have gut reactions in three seconds or less. In fact, . Quick emotional processing also happens with cascading impact. Our emotional reaction to a stimulus resounds more loudly in our brain than does our rational response, triggering the action to follow.” Dan Hill, author of Emotionomics: Leveraging Emotions for Business Success said, emotions process sensory input in only one-fifth the time our conscious, cognitive brain takes to assimilate that same input Credit: Conscious Discipline Brain State Model How you can make the most of the brain for the entire consumer journey By now I’m hoping you’ve thought about how this impacts your own consumer behaviour. Think about where you believe you sit on the spectrum of being a rational or emotional consumer (and realistically, this will vary dramatically in individuals based on product category and time in their life). You probably had a different approach to shopping when you were aged 10 vs 20 vs 30. So what does this mean to you as a marketer? There are many definitions for what a brand is, but my favourite is that . I’ve chosen the word ‘people’ intentionally, not ‘customers’ or ‘consumers’. Relationships with brands are based on a collection of exposures and experiences and associated feelings for . Those feelings get stored in your Limbic Brain to give the Reptilian Brain the inputs it needs to make the final call. The rational components of those experiences live in the Neocortex, so they’re ready to rationalise your decisions when the Reptilian Brain tells it that it’s time. brand is the unique story people recall when they think of you people Here are a few specific ways you can target the decision making part of your customers’ brains: : The Reptilian Brain is very self centric (considering it had to primarily focus on not dying, I think that’s fair). So you need to sell benefits, not features. It’s very easy for businesses to be internally rather than externally focused, so watch out for this at every touchpoint. Consider your wording carefully: it’s not about what the company provides, it’s what the customer will get. Make sure it’s clear why what you’re selling is valuable to the customer. Be customer centric : The human brain post-rationalises decisions but really, these decisions are made based on feelings and gut instinct. Yes, you’re selling a product or a service. But what are you selling? Happiness? Time? Control? Freedom? Once you know what you’re actually selling, communicating it becomes a lot easier. Think about what you’re really selling really : According to , the brain craves contrast in order to to make a decision. Make it easy for the consumer to understand the difference between products or services (don’t make them work for it). Show why it’s better Harvard Business Review : Remember that the Reptilian and Limbic Brains have no capacity for language. Therefore, according to author , they scan for what is concrete and recognisable. They don’t want to have to think, so make it easy for them by using familiar concepts and imagery to get your point across. Did you know that the for why someone chooses to click on a search result when looking for products is because they have heard of the brand before? Creating this familiarity beforehand is critical in order to get the most out of your performance marketing. Use what’s familiar Christophe Morin number one factor : Further to the last point, consistency is one of the most important factors in creating strong brands. Your team needs a deep understanding that while they may be seeing key messages again and again, the consumer will only be exposed to them a fraction of the time. When your team starts to think that it’s getting boring, that’s when it’s working! Be consistent : The brain likes and tends to overlook what’s in between. Key messages need to be at the beginning (primacy) and the end (recency) in order for you to have your best shot at successfully landing your point. Focus on the beginning and the end beginnings and endings : The Reptilian Brain responds to visuals much faster than words. First impressions will be based on visual stimuli. According to the book , after 72 hours, only 10% of people can remember information delivered in words versus 65% who can remember it if a visual is added. Make sure you put a considerable amount of time / money / effort into strong design, consistency and guardianship for your visual identity because it makes all the difference for your brand. The strongest, most memorable and iconic brands transcend language and geography to appeal to the most primal part of brains around the world. Use strong visuals Brain Rules : Emotions create electrochemical responses in the brain that impact the way we process information. As emotionally persuaded beings, it’s in the interest of companies to leverage our human nature for decision making. . This is often the area that gives them the opportunity to emotionally connect with their audiences. That being said, this only really works over the long-term if it’s genuine. Companies need to be willing to stick with their values through ups and downs, regardless if it’s trendy or not. If a company really cares about something that matters to its customers, it can use this emotional connection to drive long-term loyalty. The most to drive purchase behaviour are love, pride, fear, guilt and greed. To increase , move from rational to emotional language including storytelling, metaphors, empathetic wording, endorsements and social proof. Go heavy on emotions One of the most important components of strong brands is that they stand for something and stick to it motivating emotions positive brand recall . The truth is that if you’re an early stage startup, you, like the performance marketers I mentioned at the beginning of this article, will probably want to focus more on the end of the funnel and the prefrontal cortex that accounts for only 5% of the decision making process. You’ll do this because it’s the quickest and easiest way to gain a large enough initial audience to develop your product market fit. As you grow from startup to scale-up, this is when you can start experimenting with how you appeal to different parts of the brain throughout the consumer journey. Finally, as you move from scale-up to hypergrowth, differentiation and brand building will become your core focus. This is where . How you take advantage of the different components of the brain will vary depending on your company’s stage and what level of resources you’re willing to invest understanding how decisions are made cannot be ignored The primary point of building brand is to build brand salience — to . If you only focus on the Neocortex and end-of-the-funnel activities, you’ll only attain a fraction of the positive decisions you’re looking for. be the brand a consumer chooses when the time comes to buy Update: After writing this, I was approached to write a much more in-depth report on Consumer Neuroscience and Digital Marketing for Econsultancy. I worked with neuroscientists and leading global experts to go into much greater detail on how consumer neuroscience can be applied by digital marketers. ! Take a look

Branding and the brain: the neuroscience behind consumer decision making

A downloadable patch for the human brain was released about nine years ago and remarkably few of us have upgraded our systems yet. After you’ve downloaded the patch your brain now stores money. The patch requires you to grasp the concept of a Bitcoin but once you do you can start trading with anyone on the planet, including those that haven’t yet downloaded the patch, using only your brain. It is truly a superpower. When everyone starts using it things will change drastically. In a hostage situation for instance, anyone with a sum of money in his brain will be worth more alive than dead. Luckily, most of us won’t ever be in a hostage situation but this has far more implications than that. Imagine a population where every citizen’s brain holds a key to a Bitcoin wallet. Just the prospect of finding something of significant value in one or more of these brains would make that entire nation virtually impervious to war. It is important to remember that this is not some vision of the future. This is Pandora’s box has already been opened and there is nothing you, nor anyone else, can do about it. People often blame their reluctance to get into Bitcoin on the risks involved in doing so. Few realize that they’re exposing themselves to an even greater risk by owning any Bitcoin. Bitcoin makes taxation a lot harder. With further upgrades to the protocol and more anonymity comes even greater challenges to the taxman. In our current system, taxing money that at one point or another went through a bank is easy. All the numbers are there, ripe for harvest. Now imagine a society where no one knows who owns what in which there’s no way of getting such information, not even by force. When people hear about this for the first time they often respond with an opinion about it. “It will never work because…” or “I don’t think that…” or “They will never allow that because…” are common phrases. They think that Bitcoin can be controlled. That it’s just a matter of time before someone about it. They’re wrong. now. not does something The question is not what regulators can and should do about Bitcoin but what you can and should do about regulation. What the law in says about a certain behavior in a certain jurisdiction is one thing, what constitutes a code of conduct is another. Despite the plethora of opinions about it, we now live in a world where can opt out of political or regulatory system as long as they have a sufficiently large brain and an internet connection. Perhaps the inherited inertia of human progress is the only thing that’s keeping the current political house of cards from collapsing. Should this be the case, the risks entailed by not owning any Bitcoin would soon dwarf those entailed by holding some. moral anyone any In some sense, brains have always been wallets since they’ve always had the capacity to store valuable information. What constitutes valuable information though, has always been very subjective. That is until the invention of the consensus rules. Until Bitcoin. Until now.

Brain Wallets

Nir’s Note: This guest post is by Dr. Marc Lewis, who studies the psychology and neuroscience of addiction. After years of active research, Marc now talks, writes, and blogs about the science and experience of addiction and how people outgrow it. Visit his website here. You’ve just obliterated the last seven or eight zombies. It was a narrow escape and you’re flushed with satisfaction. But you didn’t see that horrendous creep, weaping sores and oozing pus, because he was hidden behind the dustbin in the shadow of a bombed out building. You get slimed, you’re dead. Or worse than dead. So you touch the “play again” bar at the bottom of the screen. Now you start further ahead than last time. You know you’re going to meet the slime-master again. Soon. Be prepared. Or you’ve crossed the desert and scaled the abandoned fortress, nearly to the top, moving along pathways that require hair-trigger adjustments with your joy-stick. But the wind is getting stronger by the second. It keeps pushing you back along the path, dangerously close to the edge. You make a daring dash between gusts. But you miscalculated, and the force of the wind blows you right off the edge. Down you plummet, to a plateau you crossed minutes ago. Now you have to make that climb again. But do it better. Get it right. The enormous appeal of video games can be captured by a single phrase — a phrase I’ve heard bandied about by game designers, psychologists, and other professionals who need to know how video games work. It’s . The sweet spot is the unique space that games take you to, and keep you in, where you’re winning enough “rewards” (points, pineapples, distance traveled, or zombies killed) to feel good. But also losing enough to keep trying — hard — because you really want to go all the way. the sweet spot If video games just let you win, win, win, they would get boring pretty quickly, and you’d stop playing. But if you kept losing repeatedly, the disappointment and frustration would get you to quit just as soon. The genius of game designers consists, partly, of recognizing this fundamental duality in human nature and capitalizing on it creatively. The sweet spot — where satisfaction and motivation are perfectly balanced — is what makes video games irresistible. I study the neuroscience of addiction. I write books about addiction. And in those books I try to wrap the scientific picture inside rich, real-life narratives — the experience of addiction, the thrill, the loss, and the compulsion to do it again. was about my own addiction to drugs, which bit a large chunk out of my twenties. And my nearly-finished next book uses the biographies of other addicts, ex, recovering, or still working on it. In the process of writing it, I’ve talked to a lot of addicts. And I continue to be amazed by the parallels among addictions to very different things: heroin, methamphetamine, coke, alcohol, cigarettes; and the behavioral addictions: gambling, sex, porn, food, and — of course — gaming. My first book The brain changes that take place with addiction are also remarkably similar, regardless of what you’re addicted to. At a recent conference I gazed at fMRI (brain scan) images that showed excessive activation in exactly the same spot — the nucleus accumbens (or ventral striatum, a structure inside the cortex, behind the eyeballs) — for addicts anticipating their next hit, whether of potato chips, heroin, or roulette. The nucleus accumbens also glows with activation in people with obsessive-compulsive disorder (OCD), and that’s revealing. The compulsive nature of addiction, the feeling that you just can’t stop, makes it look like a spinoff of OCD, a long-recognized psychiatric disorder. So what’s that all about? Our brains compute three things about reward: how much will we get, how soon will we get it, and how certain are we that we will in fact get it? All that computation goes on in the nucleus accumbens and its neural neighbors. The fuel that activates the nucleus accumbens is dopamine. The bigger and more immediate the expected reward, the more dopamine gets sucked up (from the midbrain) into the nucleus accumbens. And the more charged we feel as a result: wanting, reaching, straining toward that all-consuming goal — whatever it is. But here’s the catch. You might think that the more probable the reward, the more dopamine gets released as well. Wrong! (You just lost a life.) It’s actually when the probability of reward hovers at around 50% that dopamine flow is maximal. When the probability of getting it is as high as the probability of not getting it — the point of maximum uncertainty. That’s what turns us on the most. In that sense, the sweet spot is the perfect technological reflection of the brain’s fundamental formula for achievement motivation. We try hardest (and are most thrilled and enticed) when the game is hard enough but not too hard. But why are video games addictive? Start with the notion that most addictive drugs, and most addictive activities, are products of technology. Heroin comes from opium, but it passes through a lot of chemical manipulations first. Booze comes from distilleries and breweries. Porn comes from cameras. Gambling requires, at the very least, a deck of cards or a pair of dice, and the rows of slot machines in Vegas or Tokyo? — simply technological upgrades. Technology finds ways to make things more efficient or more effective. Technology evolves as an outcropping of human (cultural) evolution, serving its needs and desires with ever more refined versions of so what we want. Video games are really just another step in the evolution of games, and games are enticing because you might win but you might not. Games have been around for millenia. But video games do it so well — so — because they ride the tide of computer technology. The balance between winning and losing is continuously adjusted, according to how well you’re doing, as measured in hits and misses, gains and losses, moment by moment. If you lose too much the game gets easier. If you win too much it gets harder. Incrementally. That’s how it’s programmed. You hardly notice this insidious trick because you are so drawn in, so absorbed by it. And you are so absorbed because you are in the sweet spot. The sweet spot knows you, it finds you. It adjusts to you. efficiently In fact the sweet spot adjusts to what’s going on in your nucleus accumbens, the flow of dopamine molecules in and out of that intimate structure. As with heroin, meth, whisky, and porn, technology serves the workings of the motivational brain. We desire winning, getting, achieving — but not constantly. Even drug addicts get utterly bored when they have unlimited supplies of whatever it is. The hunt is more thrilling than the act itself. The momentous feeling of moving forward, from the present to the immediate future — the next scene, the next screen, the next belt, hit, shot, snort, or rush — is really what we’re after. And the sweet spot keeps us going for it. As a last word, I want to emphasize that “attractive” and “addictive” don’t equal “evil”. The surging wave of video game development keeps finding new ways to help people. uses the sweet spot to launch children through math instruction. games help kids understand the mechanics of evolution. As a psychologist, I’m particularly drawn to the power of video games for improving the lives of people with personal problems. Some games are geared to the learning deficits of people with attention deficit hyperactivity disorder (ADHD). And a new generation of games is aimed at those suffering from depression or anxiety disorders, enticing users through well-paced challenges and then rewarding them with skills that pay off in the real world. draws children with anxiety disorders through real fears — the ones they go to bed with at night — and then rewards them with skills for relaxing, refocusing, and persevering — skills that allow them to enter the schoolyard with more confidence the next day. Rocket Math SIMS Mindlight Mechanisms of motivation churn away at the center of the brain. And, as with other elemental forces, the manipulation of those mechanisms can serve to help rather than hurt people. The sweet spot does not have to be the brain’s velvet prison. It can also be its wellspring of growth. NOTE: This guest essay was written by Marc Lewis. The Unbelievable Future of Habit-Forming Technology If you found this post interesting, it would mean a lot to me if you could click on the “claps” icon below to let me know. That would really make my day — thanks! Nir Eyal is the author of Hooked: How to Build Habit-Forming Products and blogs about the psychology of products at NirAndFar.com .For more insights on changing behavior, join his free newsletter and receive a free workbook. <a href="https://medium.com/media/3aadb6022015a5c2e59a4edc1e7d42e7/href">https://medium.com/media/3aadb6022015a5c2e59a4edc1e7d42e7/href</a> Image Credit: Skakerman Originally published at www.nirandfar.com on August 4, 2014.

Video Games and The Motivational Brain

Why does the mind stay sharper than muscles as age advances? Why do professional athletes in several sports retire after 30? What is the difference between genes expressed elsewhere in the body and those expressed in the brain, by neurons? Why does muscle mass decrease after 30? If the mind controls muscle movement through life, does the mind have a role in muscle growth? An offspring does not inherit the language of the parent, but an offspring inherits anatomic features of the parent. What is the difference between the mechanism for language and those for the features of the parent? Both are made possible by genes, but one can be said to be indirect and the other say direct. Genes are present in all cells. They act as engines--with instructions--to make determinations. They work in muscle cells. They work across several cells and are expressed across functions. However, what is the relationship between when genes are expressed in several functions and when the brain is said to regulate those functions? Why is it possible that nurture for an offspring can result in huge differences from the parent, even if physical features are similar? What is the relationship between genes and the mind? Genes contain instructions, but it is postulated that they are almost totally regimented. Many of their instructions, conceptually, are not that flexible, so what they have to express is almost the same way it has to be expressed, without much changes--so to speak. It is theorized that what makes several functions--or options--possible is the mind. This means that genes are near fixed, but the mind runs the flexibility. A second difference is that the ability for the mind to give and receive information is greater than genes, that mostly send out instructions, though they receive information, it is not often as much as the mind, conceptually. Also, for what genes control directly and what the mind does too, directly, how do genes and the mind work together. What is the human mind? The mind is not genes nor neurons. The candidate for the human mind, conceptually, are the electrical and chemical signals of neurons, with their interactions and features in sets, in clusters of neurons across the central and peripheral nervous systems. Sets of signals have configurations [or arrangements] for which the basis of functions is held. This means that a smell, a sound, and so forth are all configurations of signals. Configuration templates arrive by the instruction of genes, but the contents are made by signals. However, there are several cases, conceptually, where genes are responsible for situations of the mind by anomalies in templates. This means that some individuals that have certain susceptibilities may have a template-induced configuration problem, such that when the problem takes place, with signals, it is at the error of genes. Simply, genes make it possible to have templates for which signals can build configurations. These templates--for several functions are generally similar--but anomalies exist within some, which may also be inherited, responsible for some kinds of addictions or mind disorders. A question is that for some of these template problems, are they from scratch or are they activated at a stage during development? Since genes are involved across cells, for development through life, it is possible to theorize that they also play roles in some of the templates for the configurations of mind, through adulthood, in many ways. This means that there could be continuous instruction passages about what templates should be, for certain functions, so that signals can then build configurations into them. In summary, genes can be said to make carvings, while signals enable a fill for those, as configurations. How Do Genes Work With The Mind? The mind is the front face of interaction with the world after birth, though the mind has already formed, conceptually before birth, it has no direct interface with the world. The mind becomes the operational source for many functions. Though genes in cells send out growth instructions, shaping cells for tissues, organs, and systems with timelines and extents, it is signals that operate at availability, while genes build availability. For example, the hand at any growth stage is by the genes, but its control or operation is by the mind. Genes can be said to have caps, theoretically, while the mind does not have caps. Even though genes lay out the possibility for what the mind can fill or build configurations for, the mind seems limitless with what it can learn, what it can be trained to do, though within the capacity of genes, but far beyond the allowance that genes have. It is theorized that a reason why sports retirement is common after 30, for many athletes, is that the stretch of genes becomes capped for certain activities around that age, even though the mind is still as sharp. Genes are also theorized to be responsible for aging, since their lack of continuous instruction for growth, unlike previously, may result in a peak then decline, by cells, of the tissue or organ. However, the mind, in use, often make it possible to keep functions active, even when growth by genes have stalled, conceptually. Mind and genes work closely together, but there is a lot that the mind decides. For example, what is the role of the mind in diseases? How do they relate to genes? Even though there are genetic anomalies responsible for some conditions, how does the mind factor? It is possible to use a conceptual model for the mind towards mapping the reach of genes against diseases. There is a recent feature, Gene therapy protects against motor neuron disease in rats, stating that, "University of Wisconsin–Madison researchers targeting a group of hereditary neurodegenerative diseases have found success using a gene therapy treatment in an animal model. The approach, which uses CRISPR-Cas9 genome editing technology, offers a unique and promising strategy that could one day treat rare but debilitating motor neuron diseases in humans. Hereditary spastic paraplegia, or HSP, is a group of movement disorders that cause progressive weakness and stiffness in the legs of people with certain inherited genetic mutations. The rare disorders usually lead to physical limitations and use of a wheelchair. Studying disease processes in animal models is an indispensable part of developing and testing any new treatments before they’re offered to humans. But scientists had historically struggled to replicate HSP’s symptoms and disease progression in animal models." There is another recent story, Aging-related genomic culprit found in Alzheimer’s disease – WashU Medicine, stating that, " Researchers at Washington University School of Medicine in St. Louis have developed a way to capture the effects of aging in the development of Alzheimer’s disease. They have devised a method to study aged neurons in the lab without a brain biopsy, an advancement that could contribute to a better understanding of the disease and new treatment strategies. The scientists transformed skin cells taken from patients with late-onset Alzheimer’s disease into brain cells called neurons. Late-onset Alzheimer’s develops gradually over many decades and only starts to show symptoms at age 65 or older. For the first time, these lab-derived neurons accurately reproduced the hallmarks of this type of dementia, including the amyloid beta buildup, tau protein deposits and neuronal cell death. By studying these cells, the researchers identified aspects of cells’ genomes — called retrotransposable elements, which change their activity as we age — in the development of late-onset Alzheimer’s disease. The findings suggest new treatment strategies targeting these factors." Why does the mind stay sharper than muscles as age advances? Why do professional athletes in several sports retire after 30? What is the difference between genes expressed elsewhere in the body and those expressed in the brain, by neurons? Why does muscle mass decrease after 30? If the mind controls muscle movement through life, does the mind have a role in muscle growth? An offspring does not inherit the language of the parent, but an offspring inherits anatomic features of the parent. What is the difference between the mechanism for language and those for the features of the parent? Both are made possible by genes, but one can be said to be indirect and the other say direct. Genes are present in all cells. They act as engines--with instructions--to make determinations. They work in muscle cells. They work across several cells and are expressed across functions. However, what is the relationship between when genes are expressed in several functions and when the brain is said to regulate those functions? Why is it possible that nurture for an offspring can result in huge differences from the parent, even if physical features are similar? What is the relationship between genes and the mind ? What is the relationship between genes and the mind Genes contain instructions, but it is postulated that they are almost totally regimented. Many of their instructions, conceptually, are not that flexible, so what they have to express is almost the same way it has to be expressed, without much changes--so to speak. It is theorized that what makes several functions--or options--possible is the mind. This means that genes are near fixed, but the mind runs the flexibility. A second difference is that the ability for the mind to give and receive information is greater than genes, that mostly send out instructions, though they receive information, it is not often as much as the mind, conceptually. Also, for what genes control directly and what the mind does too, directly, how do genes and the mind work together. What is the human mind? The mind is not genes nor neurons. The candidate for the human mind, conceptually, are the electrical and chemical signals of neurons, with their interactions and features in sets, in clusters of neurons across the central and peripheral nervous systems. Sets of signals have configurations [or arrangements] for which the basis of functions is held. This means that a smell, a sound, and so forth are all configurations of signals. Configuration templates arrive by the instruction of genes, but the contents are made by signals. templates However, there are several cases, conceptually, where genes are responsible for situations of the mind by anomalies in templates. This means that some individuals that have certain susceptibilities may have a template-induced configuration problem, such that when the problem takes place, with signals, it is at the error of genes. templates. template-induced Simply, genes make it possible to have templates for which signals can build configurations. These templates--for several functions are generally similar--but anomalies exist within some, which may also be inherited, responsible for some kinds of addictions or mind disorders. A question is that for some of these template problems, are they from scratch or are they activated at a stage during development? Since genes are involved across cells, for development through life, it is possible to theorize that they also play roles in some of the templates for the configurations of mind, through adulthood, in many ways. templates This means that there could be continuous instruction passages about what templates should be, for certain functions, so that signals can then build configurations into them. templates In summary, genes can be said to make carvings, while signals enable a fill for those, as configurations . while signals enable a fill for those, as configurations How Do Genes Work With The Mind? How Do Genes Work With The Mind? The mind is the front face of interaction with the world after birth, though the mind has already formed, conceptually before birth, it has no direct interface with the world. The mind becomes the operational source for many functions. Though genes in cells send out growth instructions, shaping cells for tissues, organs, and systems with timelines and extents, it is signals that operate at availability, while genes build availability. For example, the hand at any growth stage is by the genes, but its control or operation is by the mind. Genes can be said to have caps, theoretically, while the mind does not have caps. Even though genes lay out the possibility for what the mind can fill or build configurations for, the mind seems limitless with what it can learn, what it can be trained to do, though within the capacity of genes, but far beyond the allowance that genes have. It is theorized that a reason why sports retirement is common after 30, for many athletes, is that the stretch of genes becomes capped for certain activities around that age, even though the mind is still as sharp. Genes are also theorized to be responsible for aging, since their lack of continuous instruction for growth, unlike previously, may result in a peak then decline, by cells, of the tissue or organ. However, the mind, in use, often make it possible to keep functions active, even when growth by genes have stalled, conceptually. Mind and genes work closely together, but there is a lot that the mind decides. For example, what is the role of the mind in diseases? How do they relate to genes? Even though there are genetic anomalies responsible for some conditions, how does the mind factor? It is possible to use a conceptual model for the mind towards mapping the reach of genes against diseases. There is a recent feature, Gene therapy protects against motor neuron disease in rats , stating that, "University of Wisconsin–Madison researchers targeting a group of hereditary neurodegenerative diseases have found success using a gene therapy treatment in an animal model. The approach, which uses CRISPR-Cas9 genome editing technology, offers a unique and promising strategy that could one day treat rare but debilitating motor neuron diseases in humans. Hereditary spastic paraplegia, or HSP, is a group of movement disorders that cause progressive weakness and stiffness in the legs of people with certain inherited genetic mutations. The rare disorders usually lead to physical limitations and use of a wheelchair. Studying disease processes in animal models is an indispensable part of developing and testing any new treatments before they’re offered to humans. But scientists had historically struggled to replicate HSP’s symptoms and disease progression in animal models." Gene therapy protects against motor neuron disease in rats There is another recent story, Aging-related genomic culprit found in Alzheimer’s disease – WashU Medicine , stating that, " Researchers at Washington University School of Medicine in St. Louis have developed a way to capture the effects of aging in the development of Alzheimer’s disease. They have devised a method to study aged neurons in the lab without a brain biopsy, an advancement that could contribute to a better understanding of the disease and new treatment strategies. The scientists transformed skin cells taken from patients with late-onset Alzheimer’s disease into brain cells called neurons. Late-onset Alzheimer’s develops gradually over many decades and only starts to show symptoms at age 65 or older. For the first time, these lab-derived neurons accurately reproduced the hallmarks of this type of dementia, including the amyloid beta buildup, tau protein deposits and neuronal cell death. By studying these cells, the researchers identified aspects of cells’ genomes — called retrotransposable elements, which change their activity as we age — in the development of late-onset Alzheimer’s disease. The findings suggest new treatment strategies targeting these factors." Aging-related genomic culprit found in Alzheimer’s disease – WashU Medicine

Genes or Mind? Which is More Flexible? Roles in Aging, Sports Retirement and Growth

Alright, folks, let’s talk about the paradox of AI. I mean, yeah, sure—AI can’t exactly replace us. We’ve all read those headlines: “AI Takes Jobs,” “Chatbots to Replace Customer Service Reps,” and the occasional “AI Now Writes Better Love Letters Than You Ever Did” (not that it’s hard, buddy, you just haven’t been loved enough). But here's a thought: What if AI isn’t just taking jobs? What if it’s taking away our ability to think? Hold on, hold on—don’t throw your smart gadgets out the window just yet. Let’s dig into this, in my style, starting with. AI is Our New Best Friend or Brain Drain? Remember when we used to Google stuff? It’s like, how many calories are in an apple? Then, you’d scroll through forums and pages to find one reliable source. Sometimes it even led us through rabbit holes. Well, not that’s not so long ago. Now? Nah. Just ask ChatGPT and boom! Instant answer. Need a 5-day meal plan that caters to your gluten intolerance? A friendly AI will whip one up before you even finish your morning coffee. It’s magic, right? Like a genie that doesn’t grant three wishes but answers infinite random questions. But let’s get real: Are we getting lazier—or dare I say—dumber? AI use cases are exponentially growing every day, even as I am scripting this blog I bet some dude from his dorm is coming up with weird ideas to put AI to use in our lives. We’ve got AI tools solving our math problems, drafting our emails, and even telling us the most efficient route to Starbucks. But what happens to our brain muscles when they’re no longer lifting the weight of everyday tasks? Here is a study from The University of Sydney that sheds light on what exactly happens in the brain while performing complex tasks. When we stop challenging ourselves, our cognitive skills can atrophy, kind of like a gym membership we’ve neglected since New Year’s. It is AI vs Dopamine, Which Should be Ideally AI and Dopamine Think about it. You’re swiping through TikTok or Instagram, and those cute AI-generated memes or voiceover dogs pop up—hilarious, right? And I am not even getting into the influence of generative AI on the political advertising in 2024. Instant dopamine hit. AI-powered recommendations know exactly what makes us tick. And it’s not just TikTok. Netflix’s AI knows what you wanna watch even before you do. Now, don't get me wrong, I’m as guilty as the next person of binging an entire season of Game of Thrones (old seasons) in one weekend. But it’s a little unnerving, isn't it? The way AI reads us like an open book, feeding our brain with easy pleasures, which—according to neuroscientists—gives us quick dopamine hits, but nothing in the way of genuine satisfaction. There’s this 2022 study from the Cureus Journal of Medical Science that found an increase in screen time, correlates with Declining Physical, Psychological Health, and Sleep Patterns. It also affects the parts of the brain associated with critical thinking and emotional regulation. It’s like feeding your brain a diet of marshmallows instead of a balanced meal. Sure, it feels good, but after a while, you’re missing out on the brainy nutrients—complex problem-solving, analytical thinking, and self-reflection. Story Time Oh, and I’ve got stories, plenty of them. Ever heard about people using ChatGPT to write their wedding vows? Yeah, because nothing says commitment like a chatbot-generated “I promise to always do the dishes.” The best part? Most people won’t even notice. And maybe that’s cute in a sitcom way, but isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? When AI does the heavy lifting, our brains chill on a virtual hammock, sipping Piña Coladas while forgetting how to get back to work. So, the Cost of Convenience is leading to Brainpower on the Decline? Now, let’s talk about the real paradox here: AI makes life easier, no doubt. It’s helping us discover new drugs, predict climate change impacts, and even save the bees (and if we don’t save the bees, we're in big trouble, right?). But the question is, are we paying a hidden price for all this convenience? Take language learning, for example. Why bother memorizing vocabulary when your phone can translate for you? There’s evidence—like a study from Frontiers in Psychology—that suggests when people rely too much on digital tools for memory tasks, their brain's hippocampus (that part responsible for memory) becomes less active. In other words, it's the mental equivalent of skipping leg day. Okay not everyone is Joey when it comes to learning new languages. Or how about basic math skills? No need to know your multiplication tables when your smartphone has a built-in calculator. But get this—Christina Miles, a researcher from Old Dominion University found that students who regularly use calculators during problem-solving exercises tend to have a weaker grasp of numerical concepts compared to those who work things out manually. It’s like, we’ve got all the answers at our fingertips, but are we forgetting how to ask questions? It is just a TOOL, let’s say it Together Again Alright, let’s not get too dystopian here. I mean, AI isn’t some evil overlord plotting to turn us into mindless zombies (though The Terminator might have you think otherwise). It’s just a tool—like a hammer. It can build or it can destroy, depending on how you use it. The problem is, we’re starting to use it like a Swiss Army knife that does everything. And that means we’re relying on it for stuff we used to handle ourselves. The real question is, where do we draw the line? When do we step back and say, “Hey, maybe I should figure this out myself instead of asking the virtual assistant”? It’s like cooking—yeah, a meal kit subscription is great, but you might miss out on the joy (or pain) of learning to sauté without setting off the smoke alarm. So, Is AI Making Us Dumber? Well, let’s break it down. AI makes life easier. No doubt about it. It’s helping scientists, doctors, and even pizza delivery drivers work more efficiently. But when it comes to the rest of us—folks who aren’t busy sequencing genomes or exploring Mars—there’s a risk of cognitive atrophy. We’ve got AI doing the hard thinking while our brains sit back and enjoy the ride. And that, my friends, is the paradox. As the good ol' philosopher Descartes might say (with a bit of a twist): “I think, therefore I am… Oh, wait, I let my chatbot think for me. Am I still?” My Point is… Look, I’m not saying we should all throw our gadgets into a bonfire and go back to reading maps and growing our own food. AI is here to stay, and it’s doing a lot of good. But maybe, just maybe, we should challenge ourselves a little more. Solve that Sudoku without the app. Write a thank-you note without asking ChatGPT for a template. Teach your kid to tie their shoes without relying on YouTube. Let’s keep those brain muscles from turning to mush. After all, it’s not about ditching the tech—it’s about not letting it ditch us. Alright, folks, let’s talk about the paradox of AI. I mean, yeah, sure—AI can’t exactly replace us. We’ve all read those headlines: “AI Takes Jobs,” “Chatbots to Replace Customer Service Reps,” and the occasional “AI Now Writes Better Love Letters Than You Ever Did” (not that it’s hard, buddy, you just haven’t been loved enough). But here's a thought: What if AI isn’t just taking jobs? What if it’s taking away our ability to think? Hold on, hold on—don’t throw your smart gadgets out the window just yet. Let’s dig into this, in my style, starting with. AI is Our New Best Friend or Brain Drain? Remember when we used to Google stuff? It’s like, how many calories are in an apple? Then, you’d scroll through forums and pages to find one reliable source. Sometimes it even led us through rabbit holes. Well, not that’s not so long ago. Now? Nah. Just ask ChatGPT and boom! Instant answer. Need a 5-day meal plan that caters to your gluten intolerance? A friendly AI will whip one up before you even finish your morning coffee. It’s magic, right? Like a genie that doesn’t grant three wishes but answers infinite random questions. But let’s get real: Are we getting lazier—or dare I say—dumber? AI use cases are exponentially growing every day, even as I am scripting this blog I bet some dude from his dorm is coming up with weird ideas to put AI to use in our lives. AI use cases are exponentially growing We’ve got AI tools solving our math problems, drafting our emails, and even telling us the most efficient route to Starbucks. But what happens to our brain muscles when they’re no longer lifting the weight of everyday tasks? Here is a study from The University of Sydney that sheds light on what exactly happens in the brain while performing complex tasks. When we stop challenging ourselves, our cognitive skills can atrophy, kind of like a gym membership we’ve neglected since New Year’s. study from The University of Sydney It is AI vs Dopamine, Which Should be Ideally AI and Dopamine Think about it. You’re swiping through TikTok or Instagram, and those cute AI-generated memes or voiceover dogs pop up—hilarious, right? And I am not even getting into the influence of generative AI on the political advertising in 2024 . influence of generative AI on the political advertising in 2024 Instant dopamine hit. AI-powered recommendations know exactly what makes us tick. And it’s not just TikTok. Netflix’s AI knows what you wanna watch even before you do. Now, don't get me wrong, I’m as guilty as the next person of binging an entire season of Game of Thrones (old seasons) in one weekend. But it’s a little unnerving, isn't it? The way AI reads us like an open book, feeding our brain with easy pleasures, which—according to neuroscientists—gives us quick dopamine hits, but nothing in the way of genuine satisfaction. There’s this 2022 study from the Cureus Journal of Medical Science that found an increase in screen time, correlates with Declining Physical, Psychological Health, and Sleep Patterns . It also affects the parts of the brain associated with critical thinking and emotional regulation. It’s like feeding your brain a diet of marshmallows instead of a balanced meal. Sure, it feels good, but after a while, you’re missing out on the brainy nutrients—complex problem-solving, analytical thinking, and self-reflection. Declining Physical, Psychological Health, and Sleep Patterns Story Time Oh, and I’ve got stories, plenty of them. Ever heard about people using ChatGPT to write their wedding vows ? Yeah, because nothing says commitment like a chatbot-generated “I promise to always do the dishes.” The best part? Most people won’t even notice. And maybe that’s cute in a sitcom way, but isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? people using ChatGPT to write their wedding vows isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? isn’t it a bit unsettling that we’re outsourcing our deepest expressions to machines? When AI does the heavy lifting, our brains chill on a virtual hammock, sipping Piña Coladas while forgetting how to get back to work. So, the Cost of Convenience is leading to Brainpower on the Decline? Now, let’s talk about the real paradox here: AI makes life easier, no doubt. It’s helping us discover new drugs, predict climate change impacts, and even save the bees (and if we don’t save the bees, we're in big trouble, right?). But the question is, are we paying a hidden price for all this convenience? save the bees are we paying a hidden price for all this convenience? are we paying a hidden price for all this convenience? Take language learning, for example. Why bother memorizing vocabulary when your phone can translate for you? There’s evidence—like a study from Frontiers in Psychology —that suggests when people rely too much on digital tools for memory tasks, their brain's hippocampus (that part responsible for memory) becomes less active. In other words, it's the mental equivalent of skipping leg day. Okay not everyone is Joey when it comes to learning new languages. study from Frontiers in Psychology Or how about basic math skills? No need to know your multiplication tables when your smartphone has a built-in calculator. But get this—Christina Miles, a researcher from Old Dominion University found that students who regularly use calculators during problem-solving exercises tend to have a weaker grasp of numerical concepts compared to those who work things out manually. It’s like, we’ve got all the answers at our fingertips, but are we forgetting how to ask questions? It is just a TOOL, let’s say it Together Again Alright, let’s not get too dystopian here. I mean, AI isn’t some evil overlord plotting to turn us into mindless zombies (though The Terminator might have you think otherwise). It’s just a tool—like a hammer. It can build or it can destroy, depending on how you use it. It’s just a tool—like a hammer. It can build or it can destroy, depending on how you use it. The problem is, we’re starting to use it like a Swiss Army knife that does everything. And that means we’re relying on it for stuff we used to handle ourselves. The real question is, where do we draw the line? When do we step back and say, “Hey, maybe I should figure this out myself instead of asking the virtual assistant”? It’s like cooking—yeah, a meal kit subscription is great, but you might miss out on the joy (or pain) of learning to sauté without setting off the smoke alarm. So, Is AI Making Us Dumber? Well, let’s break it down. AI makes life easier. No doubt about it. It’s helping scientists, doctors, and even pizza delivery drivers work more efficiently. But when it comes to the rest of us—folks who aren’t busy sequencing genomes or exploring Mars—there’s a risk of cognitive atrophy. We’ve got AI doing the hard thinking while our brains sit back and enjoy the ride. And that, my friends, is the paradox. As the good ol' philosopher Descartes might say (with a bit of a twist): “I think, therefore I am… Oh, wait, I let my chatbot think for me. Am I still?” My Point is… Look, I’m not saying we should all throw our gadgets into a bonfire and go back to reading maps and growing our own food. AI is here to stay, and it’s doing a lot of good. But maybe, just maybe, we should challenge ourselves a little more. Solve that Sudoku without the app. Write a thank-you note without asking ChatGPT for a template. Teach your kid to tie their shoes without relying on YouTube. Let’s keep those brain muscles from turning to mush. After all, it’s not about ditching the tech—it’s about not letting it ditch us.

The Paradox of AI: If It Can't Replace us, Is It Making Us Dumber? 

مفارقة الذكاء الاصطناعي: إذا لم يتمكن من استبدالنا، فهل يجعلنا أكثر غباءً؟

AI এর প্যারাডক্স: এটি যদি আমাদের প্রতিস্থাপন করতে না পারে, তবে এটি কি আমাদেরকে নির্বোধ করে তুলছে? 

La paradoja de la IA: si no puede reemplazarnos, ¿nos está haciendo más tontos?

Paradoks AI: Si li pa ka ranplase nou, èske li fè nou bebè? 

AI-ի պարադոքսը. եթե այն չի կարող մեզ փոխարինել, արդյոք դա մեզ հիմա՞ր է դարձնում: 

AI のパラドックス: AI が人間に取って代わることができなければ、人間はより愚かになるのか?

ხელოვნური ინტელექტის პარადოქსი: თუ ის ვერ შეგვანაცვლებს, ის გვაქცევს სულელებს? 

Paradoxe ya AI: Soki Ekoki Ko remplacer biso te, Est-ce que Ezali Kosala Biso Dumber? 

Paradoksi i AI: Nëse nuk mund të na zëvendësojë, a na bën më të trashë? 

Kitendawili cha AI: Ikiwa Haiwezi Kuchukua Nafasi Yetu, Je, Inatufanya Wajinga? 

AI இன் முரண்பாடு: அது நம்மை மாற்ற முடியாவிட்டால், அது நம்மை ஊமையாக்குகிறதா? 

Indida ye-AI: Uma Ingakwazi Ukusithatha Indawo, Ingabe Isenza Sibe Iziduli? 

Fruit Fly Connectome: An Expansive Theory of Signals

Mass can give energy and energy can give mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. Or, simply, energy and mass are interchangeable. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have [anything moving at] the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times—chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals. The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence—through receptors, or getting taken back up, or cleared out by enzymes. Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set, these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter, that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness, self and intent, or free will. Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets, in clusters of neurons, across the central and peripheral nervous systems. There is a recent paper in Nature Communications Physics, Unveiling universal aspects of the cellular anatomy of the brain, where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." There is a recent article in The Conversation, Could quantum physics be the key that unlocks the secrets of human behaviour?, stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." Mass can give energy and energy can give mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. Or, simply, energy and mass are interchangeable. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have [anything moving at] the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times —chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. near stationary while active, at times Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals . it is the enterprise of the signals The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence— through receptors, or getting taken back up, or cleared out by enzymes . through receptors, or getting taken back up, or cleared out by enzymes Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set , these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. It is theorized that in a set A Human Mind Phase of Matter A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter , that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. postulated to be a new state of matter Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness , self and intent, or free will. attention, awareness Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. tad Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets , in clusters of neurons, across the central and peripheral nervous systems. their interactions and features, in sets There is a recent paper in Nature Communications Physics , Unveiling universal aspects of the cellular anatomy of the brain , where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." Nature Communications Physics Unveiling universal aspects of the cellular anatomy of the brain There is a recent article in The Conversation , Could quantum physics be the key that unlocks the secrets of human behaviour? , stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." The Conversation Could quantum physics be the key that unlocks the secrets of human behaviour?

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