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This one will be helpful for anyone who works with Azure Synapse Analytics. Synapse combines data warehousing, big data processing, data integration, and advanced analytics into a single, integrated solution. It offers scalable storage and computes resources, allowing you to handle large volumes of data and perform complex analytics tasks efficiently. And most important in my case it offers SQL-based querying capabilities. And here the story begins. To work with the data I would like to add some first. To make it automatically of course I have to use a program language like Python in this example. I’m looking at the Synapse docs and wondering which DB connector/driver I could use. I found the “ ”, with the help of . ODBC Driver 17 for SQL Server pyodbc ...
import pyodbc
...

def insert() -> None:

  conn = pyodbc.connect(f"Driver={{{'ODBC Driver 17 for SQL Server'}}};Server=tcp:{'host'},{'port'};Database={'database'};Uid={'username'};Pwd={'password'};Encrypt=yes;TrustServerCertificate=no;Connection Timeout=30;")
  ... It seems that the classic SQL statement should work to add data to whatever table I would like to. And it actually does insert the data. INSERT INTO f"INSERT INTO [{schema_name}].[{table_name}] 
   ({', '.join([f'[{column_name}]' for column_name in column_names])}) 
   VALUES ({', '.join(['?'] * len(column_names))});" Yeah, it is basic INSERT, it just inserts data, if there are a lot of rows this one becomes a very slow script to add the data.  First, I found property that should be set to true, so it will automatically start and close the transaction. autocommit conn.autocommit = True With it or without, when I manually commit a transaction split script, there are no significant differences. The previous change didn’t make an effect on slow insertion. Realizing the need for a more effective approach, I began exploring alternative solutions and eventually discovered the function of . This newfound function opened up a world of possibilities by enabling the execution of multiple SQL, I had thought. executemany pyodbc cursor.executemany(sql_statement, params) The function in provided some improvement, it fell short of meeting my expectations in terms of speed. Despite its ability to execute multiple SQL statements in a single call, the overall performance remained slower than desired. This limitation prompted me to continue exploring alternative methods and optimizations to enhance further the speed and efficiency of my data processing workflows. executemany pyodbc cursor.fast_executemany = True as described on some websites it should boost the script to the cosmos. But not for this case. It did nothing to execute INSERT fast. fast_executemany Eventually, after carefully reading the documentation about I realized that there is a special note about Synapse Analytics. And it said that Synapse does not support such statements at all. Azure SQL Server That means the INSERT statement supports only one row per statement. E.g.: # IS NOT SUPPORTED
INSERT INTO table_name (column_list) 
  VALUES (value_list_1), (value_list_2), ... (value_list_n); But a single insert is executable: # SUPPORTED
INSERT INTO table_name (column_list) VALUES (value_1); It was a surprise to me cause I used and it was working. And the actual reason to this is and its driver which converts script to executable. In other words, one INSERT translated to MANY INSERT: executemany pyodbc INSERT INTO table_name (column_list) VALUES (value_1);
INSERT INTO table_name (column_list) VALUES (value_2);
...
INSERT INTO table_name (column_list) VALUES (value_n); Okay, then I found a possibility to use COPY statement for fast INSERT. For this approach, I have to use the file as a proxy for data. if not os.path.exists(local_path):
    os.mkdir(local_path)

 # Create a file in the local data directory to upload and download(use it after)
 local_file_name = str(uuid.uuid4()) + ".csv"
 upload_file_path = os.path.join(local_path, local_file_name)

 # Write text to the file
 with open(upload_file_path,'w') as out:
   csv_out=csv.writer(out)
   csv_out.writerows(values) It’s not enough just to use the file on the machine where the code executes. The final script will be executed on SQL Synapse Server, the file must be available for reading. Here Azure Blob storage will be as another proxy to temporarily and privately store data files for Synapse script. try:
  # Create the BlobServiceClient object
  blob_service_client = BlobServiceClient(account_url, credential=storage_secret)
  logger.info(f"Uploading file to blob: {local_file_name} storage in schema: {destination_schema_name} table: {table_name}")
  logger.info(f"Account URL: {account_url}, Container: {storage_container}, File: {local_file_name}")
  # Using Container Client to upload blob
  container_client = blob_service_client.get_container_client(storage_container)
  with open(file=upload_file_path, mode="rb") as csv_data:
    container_client.upload_blob(local_file_name, csv_data, content_settings=ContentSettings(content_type='text/csv'))
except Exception as ex:
  logger.error(f"Error: {ex} in schema: {destination_schema_name} table: {table_name}")
  raise
finally:
  if(os.path.exists(upload_file_path) and os.path.isfile(upload_file_path)):
    os.remove(upload_file_path) Finally, write COPY INTO statement, execute and commit the transaction. file_url = f"https://{storage_identity}.blob.core.windows.net/{storage_container}/{local_file_name}"

logger.info(f"Inserting data for schema: {destination_schema_name} table: {table_name}, rows count: {len(values)}")
sql = (f"COPY INTO [{destination_schema_name}].[{table_name}] ({', '.join([f'[{c}]' for c in columns])})"
  f"FROM '{file_url}' "
  f"WITH (CREDENTIAL=(IDENTITY='Storage Account Key', SECRET='{storage_secret}'))")
cursor.execute(sql)
conn.commit() Voila, and fast insertion works. It executes much, much faster. It works fast as I assumed to be with classic . INSERT INTO table_name (column_list) VALUES (value_list_1), (value_list_2), ... (value_list_n); The gem is eventually found. After working with files and blob storage I have to keep in mind to clear everything. try:
  # Create the BlobServiceClient object
  blob_service_client = BlobServiceClient(account_url, credential=storage_secret)
  logger.info(f"Removing blob: {local_file_name} from storage in schema: {destination_schema_name} table: {table_name}")
  Using Container Client to delete blob
  container_client = blob_service_client.get_container_client(storage_container)
  container_client.delete_blobs(local_file_name)
  logger.info(f"Blob: {local_file_name} removed from storage in schema: {destination_schema_name} table: {table_name}")
except Exception as ex:
  logger.error(f"Error: {ex} in schema: {destination_schema_name} table: {table_name}")
  raise Everything works, enjoy!

How to Optimize Data Insertion Into Synapse

A Decentralized Data Marketplace It’s hard to imagine what the future will look like but we can look at the processes we have adopted over the last 10 years to get a picture of what it might hold. Artificial Intelligence and machine learning have become essential tools to make our day to day lives run a little smoother. The data we give to companies for free without even realising, helps to create a clear picture for machines to calculate a required output. From a simple purchase on Amazon, our data is collected into specific data sets and held as a preference for the next time you visit their page. Or for the next customer that purchases the item, the data collected from previous customers helps Amazon to recommend items which are usually bought along with it. We are moving into an age in which Artificial Intelligence and machine learning processes help to make our decisions for us. It’s fascinating to think how far AI and Machine learning can take our civilisation but there are two key flaws in the system today. If every piece of data created by Man or Machine was correlated into one place, for all intelligence to use, our processes would become even more efficient and allow our progress as a society to evolve faster. Also the data that is used to ultimately create profit, isn’t producing revenue for the creators of it. Synapse AI are creating a decentralized data marketplace where any type of data can be collected and used by anyone, human or machine. The creator of any data placed on the Synapse AI marketplace are rewarded in SYN, the Cryptocurrency used within the Synapse AI ecosystem. Synapse AI are essentially setting data free. Allowing it to be immediately accessible by all intelligence to allow us to create a productive and efficient future economy. Instead of major conglomerates holding data and using it for their own personal gain, the data is collected on an open network which is accessible for anyone. The system is categorized into a number of different parts. Agents, which can be human or machine can create and contribute to data pools within the system. Data pools is a single or correlated piece of data which can be given specific labels to by an agent. Data pools help to set a model, which is a characteristic or pattern of behaviour that can be picked up and understood by an agent. This in turn creates a service which is the output created through our process which can be offered and negotiated within the marketplace. The team * Dan Gailey CEO, Engineer Passionate about machine learning, and artificial intelligence, Dan is a huge proponent of ambient intelligence and how it will complement our lives. Dan holds degrees in Electrical Engineering (autonomous robotics), and Computational Chemistry. Previous work has been with the National Center for Physical Acoustics, Make Magazine, e.Ventures, in addition to launching Baqqer, Asteria, Techendo, Pule, and others. Nathan Ross COO Nathan has over a decade of experience building and growing products. He has worked with consumer products, enterprise solutions, data-focused technology, and everything in-between. Nathan’s education includes degrees in Business Management, Business Marketing, and Advertising Art Direction with a focus on Interaction Art Direction. Jamie Cushenan Blockchain Developer Starting programming at a young age, Jamie has always been fascinated by data and technology, which led him into game development after college. Since then, Jamie has continue to learn and expand his insights into machine learning, cryptocurrency and blockchain technology. Jamie has a degree from Ulster University in Software Engineering. Gil Chávez Lead Designer A long-term illustrator and graphic designer, Gil prides himself on creating new and exciting designs each and every day. Gil has helped take Synapse’s brand towards the future in this quest. Elizabeth Dixon Security Researcher Advisors * Hugh Dubberly Stanford, Apple, Netscape As the former vice president of Netscape’s Web Design and Site Integration Department, and the recent Co-founder of his own eponymous design consulting firm, Dubberly — like his favorite Greek philosopher — is a professional shiner of light into the murk and ambiguity of life. “My central concern is to figure out how design can be employed to make complex ideas visible and understandable so that we can make better products”, he says. With impeccable design and digital pedigrees, Dubberly has been on the cusp of “what’s next” since the Internet’s early days. It’s worthwhile, then, to pay attention to his present thoughts on what’s next for the business of design — and the design of business. Howard Rheingold Author, Stanford, Institute for the Future As a critic, writer and teacher, Howard Rheingold’s specialties are on the cultural, social and political implications of modern communication media such as the Internet, mobile telephony and virtual communities. His book Tools for Thought tells of the history of those who created the personal computer. About that same time he first logged in to an influential early online community called The WELL. That experienced is explored in his seminal book The Virtual Community. Jackson Palmer Founder of Dogecoin As a product, growth and data geek at Adobe by day, and coder and technologist by night, Jackson Palmer may be best known as the creator and founder of well-known cryptocurrency Dogecoin (DOGE). Introduced initially as a “joke” in 2013 to pay homage of the Shiba Inu dog from an internet meme, Dogecoin quickly developed its own online community and reached a market cap of $60 million USD in January 2014. At one point in June 2017 it’s market cap topped over $400 million USD. Social statistics * -4,188 Followers Twitter -675 Likes Facebook -19 Readers Reddit -946 Subscribers Youtube -18 Followers Medium -119 Members Telegram Slack News * September 18th, 2017 - ICO Bench For the first time in history, earn rewards by contributing data in the form of SYN tokens, which may be redeemed for services on the Synapse network or traded on most major exchanges worldwide. October 1st, 2017 - Token Market Synapse is creating a decentralized data and machine learning marketplace and exchange. This will allow anyone to contribute data, train machine learning models, and be compensated for both. October 20th, 2017 - Chipin.com aim is to create the world’s first decentralized data and intelligence machine learning marketplace for IoT and AI systems. This platform will be available to anyone from researchers who want to use data for their projects, processors, contractors as well as IoT devices and wearables that can benefit from it. Synapse AI’s The ICO * When: The ICO started October 21st, 2017 and runs until November 21st, 2017. The crowdsale will end early of the $50,000,000 (50 Million USD) hardcap is reached. Token name: Synapse AI (SYN) Total supply: 1,000,000,000 (1 Billion) SYN tokens If any tokens are remaining after the ICO the remaining tokens will be burned. Distribution: 33% -ICO 33% -Developer fund 33% -Company 1% -Token sale costs Price per token: The price variates due to the structure of the sale. Tokens are auctioned off in lots, each with a displayed number of coins in. The highest bidder on each lot receives the coins within it. Important links * The Synapse AI whitepaper. If you would like to . participate in the Synapse AI ICO My Ethereum Address -0xD9e835E886E4257F775c83F54f332d0a07E9eA83

How Synapse AI are setting data free

Nobel Prize Winner Geoffrey Hinton Explores Two Paths to Intelligence in AI Lecture

Fruit Fly Connectome: An Expansive Theory of Signals

The IISc Memristor: The Dawn of a New Era in Computing and AI Accelerators

PKC iota/lambda, CaMKII, WWC1/KIBRA, PKMzeta How do molecules organize to define one memory from another? Why does it appear like any sensory input searches for an interpretation fit or match, initially or finally? What makes any memory intelligent? Why can the memory of something become the basis for caution—or action—against something else? How do some memories last while others fade? Are there differences between the molecules that arrange what a memory is from those that make it permanent? There are two defining elements in the brain in several memory studies, the gene of a molecule and the molecule. If the gene is expressed—for the availability of the molecule—then the memory would work, so to speak. Now, if some conceptual architecture of memory were to be developed, would it be predicated on the gene or on the molecule? It is theorized that genes—like neurons—are hosts, while molecules mechanize. Simply, neurons and genes would have to be present for the molecule to be available, but it is the molecule that makes memories possible. It is like a house and the facilities in it that are useful to people. This means that in exploring the basis of human memory, molecules are ahead of genes and neurons, conceptually. Every synapse, strong or weak, has molecules at the cleft. It is theorized that the formation of these molecules differently, in a set, in a cluster of neurons, specifies one memory from the next. Simply, synapses can be described as provision pulleys, but it is the configuration of molecules that define memory. There are several so-called memory molecules, WWC1 [or KIBRA], PKC iota/lambda[PKCι/λ], CaMKII, PKMzeta [PKMζ], cGMP/PKG, cAMP, PKA, CRE, CREB-1, CREB-2, CPEB and so forth. The question is to explore what molecules structure memory and what molecules make them last. It is known that for some memories to last, repetition often is necessary. Also, consequences may ensure that long-term caution is applied. Then there could be a parallel event, say some trauma or something else that could make the memory last. There could also be an understanding of it. There is a recent paper in Science, KIBRA anchoring the action of PKMζ maintains the persistence of memory, stating that, "How can short-lived molecules selectively maintain the potentiation of activated synapses to sustain long-term memory? Here, we find kidney and brain expressed adaptor protein (KIBRA), a postsynaptic scaffolding protein genetically linked to human memory performance, complexes with protein kinase Mzeta (PKMζ), anchoring the kinase’s potentiating action to maintain late-phase long-term potentiation (late-LTP) at activated synapses. Two structurally distinct antagonists of KIBRA-PKMζ dimerization disrupt established late-LTP and long-term spatial memory, yet neither measurably affects basal synaptic transmission. Neither antagonist affects PKMζ-independent LTP or memory that are maintained by compensating PKCs in ζ-knockout mice; thus, both agents require PKMζ for their effect. KIBRA-PKMζ complexes maintain 1-month-old memory despite PKMζ turnover. Therefore, it is not PKMζ alone, nor KIBRA alone, but the continual interaction between the two that maintains late-LTP and long-term memory." If KIBRA and PKMζ have to interact to result in long-term memory, what provenance of long-term memory might this be? Repetition, consequence, parallel event of something, or an understanding of a situation? It is unlikely that molecules for memory permanence and memory configuration are totally independent because the first thing is often the form of memory before it can be permanent or temporal. For example, cars, doors, windows, shoes, books, devices, and so forth have different types, and the memory interprets all. The memory does not store all cars separately, but it often collects what is common between two or more then groups them into what can be called a thick set. It is this thick set that may become a permanent basis for the interpretation of similar memories, conceptually. So the question is how do thick sets work? Then for thin sets—where the most unique information is stored—it is likely to be temporal since specific things about every door are hardly remembered, but those of doors are often known and remembered. Molecules in formation mechanize memories, conceptually. Their placement of the memory may decide if permanent molecules may act on them, conceptually. It is the opening by formation [or configuration] molecules that may allow others to act, conceptually. There is also the role of relays from one part of the thick set to the next, or one part of the memory to the other by electrical signals that may also determine allowance of permanent molecules, conceptually. It is theorized that electrical signals in a set strike at chemical signals, to fit or match what might be available for interpretation, initially or finally. Possibility for initial and final interaction is because electrical signals split, in a set, with some going ahead of others, conceptually. Research in cellular and molecular neuroscience is necessary for neuropharmacology. However, theoretical neuroscience may lead the way to shape how to place studies as they emerge for perpendicular options against mind problems. There is a recent paper in Nature, Competitive processes shape multi-synapse plasticity along dendritic segments, stating that, "Neurons receive thousands of inputs onto their dendritic arbour, where individual synapses undergo activity-dependent plasticity. Long-lasting changes in postsynaptic strengths correlate with changes in spine head volume. The magnitude and direction of such structural plasticity - potentiation (sLTP) and depression (sLTD) - depend upon the number and spatial distribution of stimulated synapses. However, how neurons allocate resources to implement synaptic strength changes across space and time amongst neighbouring synapses remains unclear. Here we combined experimental and modelling approaches to explore the elementary processes underlying multi-spine plasticity. We used glutamate uncaging to induce sLTP at varying number of synapses sharing the same dendritic branch, and we built a model incorporating a dual role Ca2+-dependent component that induces spine growth or shrinkage. Our results suggest that competition among spines for molecular resources is a key driver of multi-spine plasticity and that spatial distance between simultaneously stimulated spines impacts the resulting spine dynamics." There is another recent paper in Nature, Images with harder-to-reconstruct visual representations leave stronger memory traces, stating that, "Much of what we remember is not because of intentional selection, but simply a by-product of perceiving. This raises a foundational question about the architecture of the mind: how does perception interface with and influence memory? Here, inspired by a classic proposal relating perceptual processing to memory durability, the level-of-processing theory, we present a sparse coding model for compressing feature embeddings of images and show that the reconstruction residuals from this model predict how well images are encoded into memory. In an open memorability dataset of scene images, we show that reconstruction error not only explains memory accuracy, but also response latencies during retrieval, subsuming, in the latter case, all of the variance explained by powerful vision-only models. We also confirm a prediction of this account with ‘model-driven psychophysics’. This work establishes reconstruction error as an important signal interfacing perception and memory, possibly through adaptive modulation of perceptual processing." Lead image source PKC iota/lambda, CaMKII, WWC1/KIBRA, PKMzeta How do molecules organize to define one memory from another? Why does it appear like any sensory input searches for an interpretation fit or match, initially or finally? What makes any memory intelligent? Why can the memory of something become the basis for caution—or action—against something else? How do some memories last while others fade? Are there differences between the molecules that arrange what a memory is from those that make it permanent? There are two defining elements in the brain in several memory studies, the gene of a molecule and the molecule. If the gene is expressed—for the availability of the molecule—then the memory would work, so to speak. Now, if some conceptual architecture of memory were to be developed, would it be predicated on the gene or on the molecule? It is theorized that genes—like neurons—are hosts, while molecules mechanize. Simply, neurons and genes would have to be present for the molecule to be available, but it is the molecule that makes memories possible. It is like a house and the facilities in it that are useful to people. This means that in exploring the basis of human memory, molecules are ahead of genes and neurons, conceptually. Every synapse, strong or weak, has molecules at the cleft. It is theorized that the formation of these molecules differently, in a set, in a cluster of neurons, specifies one memory from the next. Simply, synapses can be described as provision pulleys, but it is the configuration of molecules that define memory. There are several so-called memory molecules, WWC1 [or KIBRA], PKC iota/lambda[PKCι/λ], CaMKII, PKMzeta [PKMζ], cGMP/PKG, cAMP, PKA, CRE, CREB-1, CREB-2, CPEB and so forth. The question is to explore what molecules structure memory and what molecules make them last. It is known that for some memories to last, repetition often is necessary. Also, consequences may ensure that long-term caution is applied. Then there could be a parallel event, say some trauma or something else that could make the memory last. There could also be an understanding of it. There is a recent paper in Science , KIBRA anchoring the action of PKMζ maintains the persistence of memory , stating that, "How can short-lived molecules selectively maintain the potentiation of activated synapses to sustain long-term memory? Here, we find kidney and brain expressed adaptor protein (KIBRA), a postsynaptic scaffolding protein genetically linked to human memory performance, complexes with protein kinase Mzeta (PKMζ), anchoring the kinase’s potentiating action to maintain late-phase long-term potentiation (late-LTP) at activated synapses. Two structurally distinct antagonists of KIBRA-PKMζ dimerization disrupt established late-LTP and long-term spatial memory, yet neither measurably affects basal synaptic transmission. Neither antagonist affects PKMζ-independent LTP or memory that are maintained by compensating PKCs in ζ-knockout mice; thus, both agents require PKMζ for their effect. KIBRA-PKMζ complexes maintain 1-month-old memory despite PKMζ turnover. Therefore, it is not PKMζ alone, nor KIBRA alone, but the continual interaction between the two that maintains late-LTP and long-term memory." Science KIBRA anchoring the action of PKMζ maintains the persistence of memory If KIBRA and PKMζ have to interact to result in long-term memory, what provenance of long-term memory might this be? Repetition, consequence, parallel event of something, or an understanding of a situation? It is unlikely that molecules for memory permanence and memory configuration are totally independent because the first thing is often the form of memory before it can be permanent or temporal. For example, cars, doors, windows, shoes, books, devices, and so forth have different types, and the memory interprets all. The memory does not store all cars separately, but it often collects what is common between two or more then groups them into what can be called a thick set. It is this thick set that may become a permanent basis for the interpretation of similar memories, conceptually. So the question is how do thick sets work? Then for thin sets—where the most unique information is stored—it is likely to be temporal since specific things about every door are hardly remembered, but those of doors are often known and remembered. Molecules in formation mechanize memories, conceptually . Their placement of the memory may decide if permanent molecules may act on them, conceptually. It is the opening by formation [or configuration] molecules that may allow others to act, conceptually. mechanize memories, conceptually There is also the role of relays from one part of the thick set to the next, or one part of the memory to the other by electrical signals that may also determine allowance of permanent molecules, conceptually. It is theorized that electrical signals in a set strike at chemical signals, to fit or match what might be available for interpretation, initially or finally. Possibility for initial and final interaction is because electrical signals split, in a set, with some going ahead of others, conceptually. Research in cellular and molecular neuroscience is necessary for neuropharmacology. However, theoretical neuroscience may lead the way to shape how to place studies as they emerge for perpendicular options against mind problems. There is a recent paper in Nature , Competitive processes shape multi-synapse plasticity along dendritic segments , stating that, "Neurons receive thousands of inputs onto their dendritic arbour, where individual synapses undergo activity-dependent plasticity. Long-lasting changes in postsynaptic strengths correlate with changes in spine head volume. The magnitude and direction of such structural plasticity - potentiation (sLTP) and depression (sLTD) - depend upon the number and spatial distribution of stimulated synapses. However, how neurons allocate resources to implement synaptic strength changes across space and time amongst neighbouring synapses remains unclear. Here we combined experimental and modelling approaches to explore the elementary processes underlying multi-spine plasticity. We used glutamate uncaging to induce sLTP at varying number of synapses sharing the same dendritic branch, and we built a model incorporating a dual role Ca2+-dependent component that induces spine growth or shrinkage. Our results suggest that competition among spines for molecular resources is a key driver of multi-spine plasticity and that spatial distance between simultaneously stimulated spines impacts the resulting spine dynamics." Nature Competitive processes shape multi-synapse plasticity along dendritic segments There is another recent paper in Nature , Images with harder-to-reconstruct visual representations leave stronger memory traces , stating that, "Much of what we remember is not because of intentional selection, but simply a by-product of perceiving. This raises a foundational question about the architecture of the mind: how does perception interface with and influence memory? Here, inspired by a classic proposal relating perceptual processing to memory durability, the level-of-processing theory, we present a sparse coding model for compressing feature embeddings of images and show that the reconstruction residuals from this model predict how well images are encoded into memory. In an open memorability dataset of scene images, we show that reconstruction error not only explains memory accuracy, but also response latencies during retrieval, subsuming, in the latter case, all of the variance explained by powerful vision-only models. We also confirm a prediction of this account with ‘model-driven psychophysics’. This work establishes reconstruction error as an important signal interfacing perception and memory, possibly through adaptive modulation of perceptual processing." Nature Images with harder-to-reconstruct visual representations leave stronger memory traces Lead image source Lead image source Lead image source

How Does Human Memory Work? 

¿Kunjamsa Jaqejj Amtañapatak yanaptʼi? 

¿Cómo funciona la memoria humana?

Nola funtzionatzen du giza memoriak? 

मानव स्मृति कैसे काम करती है?

Bagaimana Memori Manusia Bekerja?

人間の記憶はどのように機能するのでしょうか?

როგორ მუშაობს ადამიანის მეხსიერება? 

인간의 기억은 어떻게 작동하는가?

Ndenge nini bokundoli ya bato esalaka? 

Како функционира човечката меморија? 

Como funciona a memória humana?

Ndangariro Yomunhu Inoshanda Sei? 

If the purpose is to understand how the the brain works, where in the brain is physicalism? Or, where in the brain is dualism? What part of the brain is materialism? How has the philosophy of mind defined the human mind, exactly for components within the cranium? There would have been eras that physicalism, dualism, materialism, panpsychism and several other philosophical takes may have held court with descriptions of how the mind or body worked, but with advances in neuroscience, there is little to no fit for their expressions. There are currently difficult problems of the mind like substance abuse, gambling addiction, mental disorders, and more. These are not problems that need more labels, but problems that, at minimum, any theory should state how the mind is operational while they are in effect and what could be done against their tides. Even within neuroscience, how the brain works is simply not neurons, neural circuits, or a population of neuronsbecause if a cluster of neurons mechanizes a function, what exactly is the cluster doing that another cluster is not? If it is stated that it is yet to be understood, then what theory of neural circuit has expressed why a [neurons] group specializes in a function? Neurons are not like changing. Neurons are close to solid [physical] forms, so it is unlikely that they are getting shaped in one direction that would indicate why they interpret a smell differently from the next, or a smell from a sound. Simply, neurons are not having direct anatomic changes to ensure a function, in contrast to another function. Even with synapses, when strong or weak, what must be definitive in every synapse? Assuming the philosophy of mind had looked closely at clusters of neurons in the last century to base many of its takes, maybe it would have been ahead of science. So far, philosophy of mind is no longer ahead of neuroscience and may not even be much linked anymore, since the philosophy chooses what it wants but the science is vacant for an advance within the cranium. The closest options to what the mind is and how the mind works is theorized to be the electrical and chemical signals of nerve cells, in sets, in clusters of neurons. It is the configuration of the signals, their interactions, and features that could be zeroed in on for how the human mind works. There is no function of neurons—for the mind—that signals are not involved in. There is no synapse—chemical or electrical—that is absent of signals. Genes are not the human mind. The environment is not the human mind. Neurons are not the human mind. Elementary particles of physics are not the human mind. Neuroglia are not the human mind as well. To answer questions about the human mind is to seek out what the mind might be in the cranium, not stray juxtapositions that, no matter how cutely described, cannot even answer for mental disorders, in the same mind for which several consciousness or philosophical stretches should define. To properly evaluate any consciousness theory or philosophical theory of mind, questions—on what the mind is [in the cranium, not the mind of the universe], how it works distinctively and how it may explain or suggest solutions to mind problems—should be sought, not generalized neuroimaging, adversarial collaboration or vague correlates. It is like trying to understand how human memory works, with mentions of long-term, short-term and others, without naming relays across memory destinations or if the configuration of any memory is different from an emotion or a feeling, or the exact forms for which memories are associated. There is a new book featured on The Transmitter, How did consciousness evolve? An excerpt from ‘A History of Bodies, Brains, and Minds: The Evolution of Life and Consciousness’, stating that, "However, life is a scientifically observable phenomenon, while our subjective experiences are not accessible to third parties. Furthermore, for conscious subjects, their own life is an experiential phenomenon as well. One alternative to get away from the mind-body conundrum has been pan-psychism, which is an extension of the identity hypothesis, proposing that some degree of consciousness is a feature of the universe like energy or gravity are. Some other researchers, particularly physicists, have moved much farther, proposing that the source of consciousness relies on quantum mechanics. In this view, quantal superpositions of states in elementary particles would collapse wave functions into (proto-) moments of experience composed of basic qualia, the fundamental ‘particles’ of consciousness. However, despite the apparent relation between consciousness and complexity, how material mechanisms become transformed into a first-person qualitative experience poses a major problem for neuroscientific research, which is conceived to deal with second-person observable phenomena. Because the unobservable nature of subjective states by second parties severely limits their scientific study, some have decided to downplay them as an irrelevant illusion generated by our complex brain networks. Whether consciousness is or is not relevant for our decisions and behavior is a debatable issue, but the illusion argument does not really explain to me how subjectivity is in fact produced." There is a recent paper in Nature, Neural populations in the language network differ in the size of their temporal receptive windows, stating that, "Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multiscale representations of linguistic input—a property that may be critical for the efficiency and robustness of language processing." If the purpose is to understand how the the brain works, where in the brain is physicalism? Or, where in the brain is dualism? What part of the brain is materialism? How has the philosophy of mind defined the human mind, exactly for components within the cranium? There would have been eras that physicalism, dualism, materialism, panpsychism and several other philosophical takes may have held court with descriptions of how the mind or body worked, but with advances in neuroscience, there is little to no fit for their expressions. There are currently difficult problems of the mind like substance abuse, gambling addiction, mental disorders, and more. These are not problems that need more labels, but problems that, at minimum, any theory should state how the mind is operational while they are in effect and what could be done against their tides. Even within neuroscience, how the brain works is simply not neurons, neural circuits, or a population of neurons because if a cluster of neurons mechanizes a function, what exactly is the cluster doing that another cluster is not? how the brain works is simply not neurons, neural circuits, or a population of neurons If it is stated that it is yet to be understood, then what theory of neural circuit has expressed why a [neurons] group specializes in a function? Neurons are not like changing. Neurons are close to solid [physical] forms, so it is unlikely that they are getting shaped in one direction that would indicate why they interpret a smell differently from the next, or a smell from a sound. Simply, neurons are not having direct anatomic changes to ensure a function, in contrast to another function. Even with synapses, when strong or weak, what must be definitive in every synapse? Assuming the philosophy of mind had looked closely at clusters of neurons in the last century to base many of its takes, maybe it would have been ahead of science. So far, philosophy of mind is no longer ahead of neuroscience and may not even be much linked anymore, since the philosophy chooses what it wants but the science is vacant for an advance within the cranium. The closest options to what the mind is and how the mind works is theorized to be the electrical and chemical signals of nerve cells, in sets, in clusters of neurons. It is the configuration of the signals, their interactions, and features that could be zeroed in on for how the human mind works. There is no function of neurons—for the mind—that signals are not involved in. There is no synapse—chemical or electrical—that is absent of signals. Genes are not the human mind . The environment is not the human mind. Neurons are not the human mind. Elementary particles of physics are not the human mind. Neuroglia are not the human mind as well. Genes are not the human mind To answer questions about the human mind is to seek out what the mind might be in the cranium, not stray juxtapositions that, no matter how cutely described, cannot even answer for mental disorders, in the same mind for which several consciousness or philosophical stretches should define. To properly evaluate any consciousness theory or philosophical theory of mind, questions—on what the mind is [in the cranium, not the mind of the universe], how it works distinctively and how it may explain or suggest solutions to mind problems—should be sought, not generalized neuroimaging, adversarial collaboration or vague correlates. It is like trying to understand how human memory works, with mentions of long-term, short-term and others, without naming relays across memory destinations or if the configuration of any memory is different from an emotion or a feeling, or the exact forms for which memories are associated. There is a new book featured on The Transmitter , How did consciousness evolve? An excerpt from ‘A History of Bodies, Brains, and Minds: The Evolution of Life and Consciousness’ , stating that, "However, life is a scientifically observable phenomenon, while our subjective experiences are not accessible to third parties. Furthermore, for conscious subjects, their own life is an experiential phenomenon as well. One alternative to get away from the mind-body conundrum has been pan-psychism, which is an extension of the identity hypothesis, proposing that some degree of consciousness is a feature of the universe like energy or gravity are. Some other researchers, particularly physicists, have moved much farther, proposing that the source of consciousness relies on quantum mechanics. In this view, quantal superpositions of states in elementary particles would collapse wave functions into (proto-) moments of experience composed of basic qualia, the fundamental ‘particles’ of consciousness. However, despite the apparent relation between consciousness and complexity, how material mechanisms become transformed into a first-person qualitative experience poses a major problem for neuroscientific research, which is conceived to deal with second-person observable phenomena. Because the unobservable nature of subjective states by second parties severely limits their scientific study, some have decided to downplay them as an irrelevant illusion generated by our complex brain networks. Whether consciousness is or is not relevant for our decisions and behavior is a debatable issue, but the illusion argument does not really explain to me how subjectivity is in fact produced." The Transmitter How did consciousness evolve? An excerpt from ‘A History of Bodies, Brains, and Minds: The Evolution of Life and Consciousness’ There is a recent paper in Nature , Neural populations in the language network differ in the size of their temporal receptive windows , stating that, "Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multiscale representations of linguistic input—a property that may be critical for the efficiency and robustness of language processing." Nature Neural populations in the language network differ in the size of their temporal receptive windows

Consciousness: Why Philosophy of Mind May No Longer Be Relevant to Neuroscience

Build a Data Warehouse Not to Drown in Your Data Lake

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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