Electrical synapses are common between inhibitory neurons in the mammalian thalamus and neocortex. Synaptic modulation, which allows flexibility of communication between neurons, has been studied e...

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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? 

How Our Disentangled Learning Framework Tackles Lifelong Learning Challenges

Authors:
(1) Sebastian Dziadzio, University of Tübingen (sebastian.dziadzio@uni-tuebingen.de);
(2) Çagatay Yıldız, University of Tübingen;
(3) Gido M. van de Ven, KU Leuven;
(4) Tomasz Trzcinski, IDEAS NCBR, Warsaw University of Technology, Tooploox;
(5) Tinne Tuytelaars, KU Leuven;
(6) Matthias Bethge, University of Tübingen. Table of Links Abstract and 1. Introduction 2. Two problems with the current approach to class-incremental continual learning 3. Methods and 3.1. Infinite dSprites 3.2. Disentangled learning 4. Related work 4.1. Continual learning and 4.2. Benchmarking continual learning 5. Experiments 5.1. Regularization methods and 5.2. Replay-based methods 5.3. Do we need equivariance? 5.4. One-shot generalization and 5.5. Open-set classification 5.6. Online vs. offline Conclusion, Acknowledgments and References Supplementary Material 5.1. Regularization methods In this section, we compare our method to standard regularization methods: Learning without Forgetting (LwF) [17] and Synaptic Intelligence (SI) [39]. We use implementations from Avalanche [21]. We provide details of the hyperparameter choice in the supplementary material. As shown in Fig. 4, such regularization methods are ill-equipped to deal with the class-incremental learning scenario and perform no better than naive fine-tuning. 5.2. Replay-based methods Replay-based methods retain a subset of past training data that is then mixed with the current training data for every task. While this can be a viable strategy to preserve accuracy over many tasks, it results in ever-growing memory and computation requirements, unless the buffer size is bounded. In this section, we investigate the effect of buffer size on performance for standard experience replay with reservoir sampling. While there are replay-based methods that improve on this baseline, we are interested in investigating the fundamental limits of rehearsal over long time horizons and strip away the confounding effects of data augmentation, generative replay, sampling strategies, pseudo-rehearsal etc. Figure 5 shows test accuracy for experience replay with different buffer sizes. Storing enough past samples lets the model maintain high test accuracy, but even with a buffer of 20,000 images the performance eventually starts to deteriorate. Note that after 200 tasks a balanced buffer will only contain 10 samples per class. A note on implementation In an attempt to make the replay baseline stronger, we first add the data from the current task to the buffer and then train the model exclusively on the buffer, effectively discounting the influence of the current task over time [28]. A more standard version of experience replay would mix current and past data in equal proportions in each mini-batch, likely leading to diminished performance on previous tasks. The supplementary material includes a comparison to this other replay baseline, as well as to a version of experience replay with no memory constraint but with a compute budget matching our approach. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sebastian Dziadzio, University of Tübingen (sebastian.dziadzio@uni-tuebingen.de); (2) Çagatay Yıldız, University of Tübingen; (3) Gido M. van de Ven, KU Leuven; (4) Tomasz Trzcinski, IDEAS NCBR, Warsaw University of Technology, Tooploox; (5) Tinne Tuytelaars, KU Leuven; (6) Matthias Bethge, University of Tübingen. Authors: Authors: (1) Sebastian Dziadzio, University of Tübingen (sebastian.dziadzio@uni-tuebingen.de); (2) Çagatay Yıldız, University of Tübingen; (3) Gido M. van de Ven, KU Leuven; (4) Tomasz Trzcinski, IDEAS NCBR, Warsaw University of Technology, Tooploox; (5) Tinne Tuytelaars, KU Leuven; (6) Matthias Bethge, University of Tübingen. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Two problems with the current approach to class-incremental continual learning 2. Two problems with the current approach to class-incremental continual learning 3. Methods and 3.1. Infinite dSprites 3. Methods and 3.1. Infinite dSprites 3.2. Disentangled learning 3.2. Disentangled learning 4. Related work 4.1. Continual learning and 4.2. Benchmarking continual learning 4.1. Continual learning and 4.2. Benchmarking continual learning 5. Experiments 5. Experiments 5.1. Regularization methods and 5.2. Replay-based methods 5.1. Regularization methods and 5.2. Replay-based methods 5.3. Do we need equivariance? 5.3. Do we need equivariance? 5.4. One-shot generalization and 5.5. Open-set classification 5.4. One-shot generalization and 5.5. Open-set classification 5.6. Online vs. offline 5.6. Online vs. offline Conclusion, Acknowledgments and References Conclusion, Acknowledgments and References Supplementary Material Supplementary Material 5.1. Regularization methods In this section, we compare our method to standard regularization methods: Learning without Forgetting (LwF) [17] and Synaptic Intelligence (SI) [39]. We use implementations from Avalanche [21]. We provide details of the hyperparameter choice in the supplementary material. As shown in Fig. 4, such regularization methods are ill-equipped to deal with the class-incremental learning scenario and perform no better than naive fine-tuning. 5.2. Replay-based methods Replay-based methods retain a subset of past training data that is then mixed with the current training data for every task. While this can be a viable strategy to preserve accuracy over many tasks, it results in ever-growing memory and computation requirements, unless the buffer size is bounded. In this section, we investigate the effect of buffer size on performance for standard experience replay with reservoir sampling. While there are replay-based methods that improve on this baseline, we are interested in investigating the fundamental limits of rehearsal over long time horizons and strip away the confounding effects of data augmentation, generative replay, sampling strategies, pseudo-rehearsal etc. Figure 5 shows test accuracy for experience replay with different buffer sizes. Storing enough past samples lets the model maintain high test accuracy, but even with a buffer of 20,000 images the performance eventually starts to deteriorate. Note that after 200 tasks a balanced buffer will only contain 10 samples per class. A note on implementation In an attempt to make the replay baseline stronger, we first add the data from the current task to the buffer and then train the model exclusively on the A note on implementation buffer, effectively discounting the influence of the current task over time [28]. A more standard version of experience replay would mix current and past data in equal proportions in each mini-batch, likely leading to diminished performance on previous tasks. The supplementary material includes a comparison to this other replay baseline, as well as to a version of experience replay with no memory constraint but with a compute budget matching our approach. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How Effective Are Standard Regularization and Replay Methods for Class-Incremental Learning? 

Theoretical Physics: Gaps in Reality, Existence,  Panpsychism, and Biology

How To Learn Anything Fast

কিভাবে দ্রুত যেকোনো কিছু শিখতে হয়

Wie man schnell etwas lernt

Cómo aprender cualquier cosa rápido

Comment apprendre n'importe quoi rapidement

कुछ भी तेजी से कैसे सीखें

何でも早く学ぶ方法

무엇이든 빨리 배우는 방법

Como aprender qualquer coisa rapidamente

Как выучить что угодно быстро

Her Şey Hızlı Nasıl Öğrenilir?

Làm thế nào để học mọi thứ nhanh chóng

如何快速学习任何东西

Video Scene Location Recognition Using AI: Methodology

There is a new paper in Nature—Communications Physics, Unveiling universal aspects of the cellular anatomy of the brain, where the authors wrote, "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. The nature of the relationship between structural criticality in the cellular structure of the brain and observations of criticality in neuronal avalanches remains an exciting direction to explore." What, in the brain, could be in phase transition? Could it be blood vessels, glia, microglia, neurons, gray matter, white matter, or others? It is proposed here that the fusion of electrical and chemical signals of neurons is what undergoes a 'critical point' and 'structural phase transition' to mechanize functions. Electrical signals are often imaged to strike at synapses—where chemical signals are present. It is postulated here that in a set [of electrical and chemical signals] in clusters of neurons, electrical signals strike to fuse briefly with chemical signals, to access the formation or configuration that the chemical signals hold. Simply, electrical signals [via action potentials] are not just triggering chemical signals [or opening of synaptic vesicles]. In sets, signals are interacting. Conceptually, the human mind is theorized to be the collection of electrical and chemical signals, with their interactions and features, in sets in clusters of neurons. Interactions define how they operate functions. Features grade or qualify those functions. Functions are mainly memory, emotions, feelings, and regulation of internal senses. Features are attention, awareness, self or subjectivity, and free will or intent. Chemical signals, in sets, contribute rations that become the configuration for functions. Variations in contributions of rations become how they are graded. Electrical signals relay and deliver summaries from one set to the next. When a set of electrical signals strike at a set of chemical signals, they mesh into a new state, which is a mix of ions [electrical signals], molecules [chemical signals], and a different phase—or particle—from both, which is proposed to be a new classical state of matter. For several sets, access is simply made by this striking and fusing. For others—where learning is made—turns that define neuroplasticity structure the new formation or configuration. This is detailed in the action potentials neurotransmitters theory of consciousness. In summary, it is theorized that electrical and chemical signals interact to mechanize functions. When they interact, they result in a mix that is a fraction of both signals and a new phase, for which functions are based. There is a new paper in Nature—Communications Physics , Unveiling universal aspects of the cellular anatomy of the brain , where the authors wrote, "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. The nature of the relationship between structural criticality in the cellular structure of the brain and observations of criticality in neuronal avalanches remains an exciting direction to explore." Nature—Communications Physics Unveiling universal aspects of the cellular anatomy of the brain What, in the brain, could be in phase transition? Could it be blood vessels, glia, microglia, neurons, gray matter, white matter, or others? It is proposed here that the fusion of electrical and chemical signals of neurons is what undergoes a 'critical point' and 'structural phase transition' to mechanize functions. Electrical signals are often imaged to strike at synapses—where chemical signals are present. It is postulated here that in a set [of electrical and chemical signals] in clusters of neurons, electrical signals strike to fuse briefly with chemical signals, to access the formation or configuration that the chemical signals hold. Simply, electrical signals [via action potentials] are not just triggering chemical signals [or opening of synaptic vesicles]. In sets, signals are interacting. Conceptually, the human mind is theorized to be the collection of electrical and chemical signals, with their interactions and features, in sets in clusters of neurons. Interactions define how they operate functions. Features grade or qualify those functions. Functions are mainly memory, emotions, feelings, and regulation of internal senses. Features are attention, awareness, self or subjectivity, and free will or intent. Chemical signals, in sets, contribute rations that become the configuration for functions. Variations in contributions of rations become how they are graded. Electrical signals relay and deliver summaries from one set to the next. When a set of electrical signals strike at a set of chemical signals, they mesh into a new state, which is a mix of ions [electrical signals], molecules [chemical signals], and a different phase—or particle—from both, which is proposed to be a new classical state of matter. For several sets, access is simply made by this striking and fusing. For others—where learning is made—turns that define neuroplasticity structure the new formation or configuration. This is detailed in the action potentials neurotransmitters theory of consciousness . action potentials neurotransmitters theory of consciousness In summary, it is theorized that electrical and chemical signals interact to mechanize functions. When they interact, they result in a mix that is a fraction of both signals and a new phase, for which functions are based. both new

Particle Physics - Brain Science - A New Classical State of Matter - of the Human Mind

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