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Holographic Memory: A Novel Model of Information Processing by Neuronal Microcircuits
Abstract
In the proposed model, each cortical minicolumn possesses a complete copy of the memory characteristic of the entire cortical zone to which it belongs. Hence, the cortex has holographic properties, where each fragment of an information carrier contains not just a part of the information but a complete copy. It is argued that each minicolumn encodes the new information using its own interpretation. Such transcoding is equivalent to considering the source information in a particular context. The model suggests that the cortex zone is a space of possible contexts for interpretation. The presence of a full copy of the memory at each minicolumn allows to determine which context is most suitable for interpreting the current information. Possible biological mechanisms are discussed that could implement the model components, including information processing algorithms that enable high computing power.
... We propose another interpretation that allows the creation of an original approach for data generalization and recognition tasks, inspired by more relevant information to today's research. Roots of our interpretation are described in detail in [1]. ...
... Modern vision of neurons' structure has gone far from the vision in times of Hubel and Wiesel. Possible mechanisms of these types of memory will be described in [1]. The sample architecture in this article is based on the TensorFlow framework and the classic perceptron conception. ...
Cortical minicolumns are considered a model of cortical organization. Their function is still a source of research and not reflected properly in modern architecture of nets in algorithms of Artificial Intelligence. We assume its function and describe it in this article. Furthermore, we show how this proposal allows to construct a new architecture, that is not based on convolutional neural networks, test it on MNIST data and receive close to Convolutional Neural Network accuracy. We also show that the proposed architecture possesses an ability to train on a small quantity of samples. To achieve these results, we enable the minicolumns to remember context transformations.
... For every cell, its information matrix (PIF) integrates with its cellular senome and electrome, interacting with various internal and external electromagnetic fields [91,103]. Pertinently, the integration of the electrome has been productively modeled based on a neural network architecture that integrates information processing through an addressable holographic memory space proposed to underlie human brain activity [52,104]. ...
Cellular measurement is a crucial faculty in living systems, and exaptations are acknowledged as a significant source of evolutionary innovation. However, the possibility that the origin of biological order is predicated on an exaptation of the measurement of information from the abiotic realm has not been previously explored. To support this hypothesis, the existence of a universal holographic relational information space-time matrix is proposed as a scale-free unification of abiotic and biotic information systems. In this framework, information is a universal property representing the interactions between matter and energy that can be subject to observation. Since observers are also universally distributed, information can be deemed the fundamental fabric of the universe. The novel concept of compartmentalizing this universal N-space information matrix into separate N-space partitions as nodes of informational density defined by Markov blankets and boundaries is introduced, permitting their applicability to both abiotic and biotic systems. Based on these N-space partitions, abiotic systems can derive meaningful information from the conditional settlement of quantum entanglement asymmetries and coherences between separately bounded quantum informational reference frames sufficient to be construed as a form of measurement. These conditional relationships are the precursor of the reiterating nested architecture of the N-space-derived information fields that characterize life and account for biological order. Accordingly, biotic measurement and biological N-space partitioning are exaptations of preexisting information processes within abiotic systems. Abiotic and biotic states thereby reconcile as differing forms of measurement of fundamental universal information. The essential difference between abiotic and biotic states lies within the attributes of the specific observer/detectors, thereby clarifying several contentious aspects of self-referential consciousness.
... The torus geometry also potentially opens a fourth spatial dimension with a symmetric time aspect, in which the particular protein can be directed to a final functional configuration in the context of the integral cell and whole cell context. This theory finds support in the earlier used computational neural network models, in which integral forward processing of information in a neuronal network, leads to a growing addressable holographic memory space [23]. The global and local excitation mechanisms and the presence of various cytoplasmic factors within the cell, clearly imply that "a life protein molecule is never alone", as depicted in Table 1. ...
The current geometric and thermodynamic approaches in protein folding
studies do not provide a definite solution to understanding mechanisms of
folding of biological proteins. A major problem is that the protein is first synthesized
as a linear molecule that subsequently must reach its native configuration
in an extremely short time. Hydrophobicity-hydrophilicity models and
random search mechanism cannot explain folding to the 3-D functional form
in less than 1 second, as it occurs in the intact cell. We propose an integral
approach, based on the embedding of proteins in the whole cellular context
under the postulate: a life protein is never alone. In this concept the protein
molecule is influenced by various long and short distance force fields of nature
such as coherent electromagnetic waves and zero-point energy. In particular,
the role of solitons is reviewed in relation to a novel GM-scale biophysical
principle, revealed by us. This recent finding of a set of discrete EM frequency
bands, that either promote or endanger life conditions, could be a key
in further studies directed at the morphogenetic aspects of protein folding in a
biological evolutionary context. In addition, an alternative hypothesis is presented
in which each individual cell may store integral 3-D information holographically
at the virtual border of a 4-D hypersphere that surrounds each
living cell, providing a field receptive memory structure that is instrumental in
guiding the folding process towards coherently oscillating protein networks
that are crucial for cell survival
... The torus geometry also potentially opens a fourth spatial dimension with a symmetric time aspect, in which the particular protein can be directed to a final functional configuration in the context of the integral cell and whole cell context. This theory finds support in the earlier used computational neural network models, in which integral forward processing of information in a neuronal network, leads to a growing addressable holographic memory space [23]. The global and local excitation mechanisms and the presence of various cytoplasmic factors within the cell, clearly imply that "a life protein molecule is never alone", as depicted in Table 1. ...
The current geometric and thermodynamic approaches in protein folding mechanisms do not provide a definite solution to the modeling of folding of biological proteins. This in spite of the fact that in various studies impressive progress was made. A major problem (the so called Levinthal paradox) is that the protein is first synthesized as a linear molecule, that subsequently must reach its native configuration in a short time. In particular, computational biology has shown that the problem of folding based on H-P (hydrophobic-hydrophilic) model, is an extremely complex process. In particular random search mechanism cannot provide real-time folding on the order of 1 sec as it occurs in the cell. Importantly, the protein can only perform its functions in this (often single) configuration. In order to present a complimentary view, we propose a rather versatile and holistic approach to the problem, based on the embedding of proteins in an integral cellular context, exhibiting, apart from the known genomic and proteomic modalities, a well defined " electromic " aspect. We consider the protein molecule as being influenced by the various long-and short distance force fields of nature such as gravity, dark energy, zero-point energy and electromagnetism, in addition to intrinsic vibratory states of macromolecules that generate coherent excitations in the cell: a life protein is never alone. In particular, there is abundant information in literature on the role of soliton waves (phonon/electron quasi particles) that can be instrumental as guiding elements in the various steps in the folding process. An alternative hypothesis is presented in which we envision a fractal toroidal operator structure to collect and integrate the abovementioned field information of cells, including their proteins and stores this in a holographic manner on a 2-D virtual screen at the border of a 4-D hypersphere that surrounds each individual cell. The latter implies a field-receptive memory structure, that can faithfully collect and store the integral history and present state of the particular cell. Our recent finding of a set of discrete EM frequency bands that either promote or endanger life conditions could be a key in further studies directed at the morphogenetic aspects of protein folding in a biological evolutionary context.
In the paper, the biological neural network models are analyzed with a purpose to solve the problems of segmentation and pattern recognition when applied to the bio-liquid facies obtained by the cuneiform dehydration method. The peculiarities of the facies’ patterns and the key steps of their digital processing are specified in the frame of the pattern recognition. Feasibility of neural network techniques for the different image data level digital processing is reviewed as well as for image segmentation. The real-life biological neural network architecture concept is described using the mechanisms of the electrical input-output membrane voltage and both induced and endogenic (spontaneous) activities of the neural clusters when spiking. The mechanism of spike initiation is described for metabotropic and ionotropic receptive clusters with the nature of environmental exciting impact specified. Also, the mathematical models of biological neural networks that comprise ot only functional nonlinearities but the hysteretic ones are analyzed and the reasons are given for preference of the mathematical model with delay differential equations is chosen providing its applicability for modeling a single neuron and neural network as well.
В работе рассматривается применение моделей биологической нейронной сети для сегментации изображения фации биожидкости, полученной методом клиновидной дегидратации. Выделены основные характерные особенности, присущие паттернам фаций биожидкостей, а также основные этапы их цифровой обработки в рамках задачи распознавания образов. Проведен анализ использования искусственных нейронных сетей для цифровой обработки изображений для разных уровней представления данных; сделан обзор основных нейросетевых методов сегментации. Описан принцип построения биологически достоверных искусственных нейронных сетей, использующих механизмы изменения мембранного потенциала нейронов и учитывающих при генерации спайка как вызванную активность, так и эндогенную (спонтанную) активность нейронных кластеров. Описан механизм инициации спайка для метаботропных и ионотропных рецептивных кластеров с указанием природы запускающего внешнего воздействия. Проведен анализ существующих математических моделей биологических нейросетей, содержащих помимо обычных функциональных нелинейностей нелинейности гистерезисной природы. Сделан выбор в пользу математической модели, использующей дифференциальные уравнения с запаздыванием, которые могут быть применены как для описания отдельного биологического нейрона, так и для описания работы нейронной сети.
Every area of knowledge builds its descriptions using concepts. At the same time, the definition of concepts given through their characteristic features is widespread. On this basis, both basic mathematical and many philosophical concepts are built. The concepts which a person uses are subject to similar properties, and their nature is the nature of definitions. Numerous attempts to create strong artificial intelligence are based on the corresponding paradigm. The article attempts to substantiate the need to use the contextual-semantic paradigm to explain the work of the natural brain and to create a strong artificial intelligence. A formal model describing the meaning is presented, and its connection with the known data on the functioning of the brain is given. It is shown that a context can be created around each concept, which can be the bearer of the concept's meaning. The context allows one to move away from using a set of features to recognize the phenomenon behind a concept. The context turns out to be a point of view associated with the concept, in which the description of the surrounding world changes. Knowing the rules of these changes, one can not only model different points of view, but also determine which of them create adequate interpretations. At the same time, the presence of an adequate interpretation in the context of the phenomenon serves as a criterion for the presence of this phenomenon.
Artificial intelligence becomes an integral part of human life. At the same time, modern widely used approaches, which work successfully due to the availability of enormous computing power, based on ideas about the work of the brain, suggested more than half a century ago. The proposed model describes the general principles of information processing by the human brain, taking into account the latest achievements. The neuroscientific grounding of this model and its applicability in the creation of AGI or Strong AI are discussed in the article. In this model, the cortical minicolumn is the primary computing processor that works with the semantic description of information. The minicolumn transforms incoming information into its interpretation to a specific context. In this way, a parallel verification of hypotheses of information interpretations is provided when comparing them with information in the memory of each minicolumn of the cortical zone, and, at the same time, determining a significant context is the information transformation rule. The meaning of information is defined as an interpretation that is close to the information available in the memory of a minicolumn. The behavior is a result of modeling of possible situations. Using this approach will allow creating a strong AI or AGI.
Autoencoders receive latent models of input data. It was shown in recent works that they also estimate probability density functions of the input. This fact makes using the Bayesian decision theory possible. If we obtain latent models of input data for each class or for some points in the space of parameters in a parameter estimation task, we are able to estimate likelihood functions for those classes or points in parameter space. We show how the set of autoencoders solves the recognition problem. Each autoencoder describes its own model or context, a latent vector that presents input data in the latent space may be called treatment in its context. Sharing latent spaces of autoencoders gives a very important property that is the ability to separate treatment and context where the input information is treated through the set of autoencoders. There are two remarkable and most valuable results of this work: a mechanism that shows a possible way of forming abstract concepts and a way of reducing dataset's size during training. These results are confirmed by tests presented in the article.
Virtually unknown just a decade ago, GTP-binding proteins (G proteins) have become a major focus of current research. This family of closely related proteins transduce extracellular signals (such as hormones, neurotransmitters and sensory stimuli) into effector responses (1,2). It is now evident that ion channel permeability is one such effector response. In fact, the striking increase in the frequency of reports that demonstrate G protein-regulated ion channel function suggests that channels whose permeability mechanism can be altered by a G protein-mediated process may be more the rule than the exception. It is well-known that the cAMP-dependent modulation of ion channels is under the control of G proteins that regulate adenylate cyclase activity(3,4). However recent studies demonstrate that G proteins also transduce agonist-induced changes in channel activity that do not involve adenylate cyclase. It is on this aspect of G protein signal transduction that this review will focus.
INTRODUCTIONThe ability of multilayer back-propagation networks to learn complex, high-dimensional, nonlinearmappings from large collections of examples makes them obvious candidates for imagerecognition or speech recognition tasks (see PATTERN RECOGNITION AND NEURALNETWORKS). In the traditional model of pattern recognition, a hand-designed featureextractor gathers relevant information from the input and eliminates irrelevant variabilities.A trainable classifier then categorizes the...
Two-way communication between neurons and nonneural cells called glia is essential for axonal conduction, synaptic transmission,
and information processing and thus is required for normal functioning of the nervous system during development and throughout
adult life. The signals between neurons and glia include ion fluxes, neurotransmitters, cell adhesion molecules, and specialized
signaling molecules released from synaptic and nonsynaptic regions of the neuron. In contrast to the serial flow of information
along chains of neurons, glia communicate with other glial cells through intracellular waves of calcium and via intercellular
diffusion of chemical messengers. By releasing neurotransmitters and other extracellular signaling molecules, glia can affect
neuronal excitability and synaptic transmission and perhaps coordinate activity across networks of neurons.
The ability to find one's way depends on neural algorithms that integrate information about place, distance and direction, but the implementation of these operations in cortical microcircuits is poorly understood. Here we show that the dorsocaudal medial entorhinal cortex (dMEC) contains a directionally oriented, topographically organized neural map of the spatial environment. Its key unit is the 'grid cell', which is activated whenever the animal's position coincides with any vertex of a regular grid of equilateral triangles spanning the surface of the environment. Grids of neighbouring cells share a common orientation and spacing, but their vertex locations (their phases) differ. The spacing and size of individual fields increase from dorsal to ventral dMEC. The map is anchored to external landmarks, but persists in their absence, suggesting that grid cells may be part of a generalized, path-integration-based map of the spatial environment.
Brain-machine interfaces (BMIs) establish unidirectional and bidirectional communication channels between the brain and assistive devices, such as wheelchairs, limb prostheses and computers. BMI technologies can also link areas within the brain and even individual brains. BMI approach holds promise to provide effective treatment for a range of neurological conditions. Moreover, BMIs can be utilized to augment brain function in healthy individuals. Here we consider three broadly defined BMI types: Motor, sensory and cognitive. For these BMI types, both noninvasive and invasive methods have been employed for neural recordings and stimulation. While original BMIs were implemented at the level of brain macrocircuits, a recent trend was to develop BMIs that utilize neuronal microcircuits.
1.
Coexisting with oxytocin or vasopressin in the cell bodies and nerve terminals of the hypothalamic-neurohypophysial system are smaller amounts of other peptides. For a number of these copeptides there is strong evidence of corelease with the major magnocellular hormones. Guided by the location of their specific receptors we have studied the effects of three copeptides, dynorphin, cholecystokinin (CCK), and corticotropin releasing hormone (CRH), on the secretion of oxytocin and vasopressin from isolated rat neural lobe or neurointermediate lobe preparationsin vitro.
2.
Dynorphin is coreleased with vasopression from neural lobe nerve terminals and acts on neural lobe kappa-opiate receptors to inhibit the electrically stimulated secretion of oxytocin. Naloxone augments oxytocin release from the neural lobe in a manner directly proportional to the amount of vasopressin (and presumably dynorphin) released.
3.
Cholecystokinin, coreleased with oxytocin by neural lobe terminals, has been shown to have high-affinity receptors located in the NL and to stimulate secretion of both oxytocin and vasopressin. CCK's secretagogue effect was independent of electrical stimulation and extracellular Ca2+ and was blocked by an inhibitor of protein kinase C.
4.
CRH, coreleased with OT from the neural lobe, has receptors in the intermediate lobe of the pituitary, but not in the neural lobe itself. CRH stimulates the secretion of oxytocin and vasopressin from combined neurointermediate lobes but not from isolated neural lobes. Intermediate lobe peptides, alpha and gamma melanocyte stimulating hormone, induced secretion of oxytocin and vasopressin from isolated neural lobes. Their effect was, like that of CCK, independent of electrical stimulation and extracellular Ca2+ and blocked by an inhibitor of protein kinase C.
5.
Among the CRH-producing parvocellular neurons of the paraventricular nucleus, in the normal rat, approximately half also produce and store vasopressin. After removal of glucocorticoid influence by adrenalectomy, virtually all of the CRH neurons contain vasopressin.
6.
The two subtypes of CRH neurosecretory cells found in the normal rat possess different topographical distributions in the paraventricular nucleus, suggesting the possibility of differential innervation. Stress selectively activates the vasopression containing subpopulation of CRH neurons, indicating that there are separate channels of regulatory input controlling the two components of the parvocellular CRH neurosecretory system.
In this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash-coding methods are examined and compared with a particular conventional hash-coding method. The computational factors considered are the size of the hash area (space), the time required to identify a message as a nonmember of the given set (reject time), and an allowable error frequency.
The new methods are intended to reduce the amount of space required to contain the hash-coded information from that associated with conventional methods. The reduction in space is accomplished by exploiting the possibility that a small fraction of errors of commission may be tolerable in some applications, in particular, applications in which a large amount of data is involved and a core resident hash area is consequently not feasible using conventional methods.
In such applications, it is envisaged that overall performance could be improved by using a smaller core resident hash area in conjunction with the new methods and, when necessary, by using some secondary and perhaps time-consuming test to “catch” the small fraction of errors associated with the new methods. An example is discussed which illustrates possible areas of application for the new methods.
Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.
The hippocampus is critical to remembering the flow of events in distinct experiences and, in doing so, bridges temporal gaps between discontiguous events. Here, we report a robust hippocampal representation of sequence memories, highlighted by "time cells" that encode successive moments during an empty temporal gap between the key events, while also encoding location and ongoing behavior. Furthermore, just as most place cells "remap" when a salient spatial cue is altered, most time cells form qualitatively different representations ("retime") when the main temporal parameter is altered. Hippocampal neurons also differentially encode the key events and disambiguate different event sequences to compose unique, temporally organized representations of specific experiences. These findings suggest that hippocampal neural ensembles segment temporally organized memories much the same as they represent locations of important events in spatially defined environments.
Briefly describes frame systems as a formalism for representing knowledge and then concentrates on the issue of what the content of knowledge should be in specific domains. Argues that vision should be viewed symbolically with an emphasis on forming expectations and then using details to fill in slots in those expectations. Discusses the enormous problem of the volume of background common sense knowledge required to understand even very simple natural language texts and suggests that networks of frames are a reasonable approach to represent such knowledge. Discusses the concept of expectation further including ways to adapt to and understand expectation failures. Argues that numerical approaches to knowledge representation are inherently limited.
A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by "learning without a teacher", and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname "neocognitron". After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consists of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of "S-cells", which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of "C-cells" similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any "teacher" during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cells of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.
In the mammalian brain, astrocytes modulate neuronal function, in part, by synchronizing neuronal firing and coordinating synaptic networks. Little, however, is known about how this is accomplished from a structural standpoint. To investigate the structural basis of astrocyte-mediated neuronal synchrony and synaptic coordination, the three-dimensional relationships between cortical astrocytes and neurons was investigated. Using a transgenic and viral approach to label astrocytes with enhanced green fluorescent protein, we performed a three-dimensional reconstruction of astrocytes from tissue sections or live animals in vivo. We found that cortical astrocytes occupy nonoverlapping territories similar to those described in the hippocampus. Using immunofluorescence labeling of neuronal somata, a single astrocyte enwraps on average four neuronal somata with an upper limit of eight. Single-neuron dye-fills allowed us to estimate that one astrocyte contacts 300-600 neuronal dendrites. Together with the recent findings showing that glial Ca2+ signaling is restricted to individual astrocytes in vivo, and that Ca2+ signaling leads to gliotransmission, we propose the concept of functional islands of synapses in which groups of synapses confined within the boundaries of an individual astrocyte are modulated by the gliotransmitter environment controlled by that astrocyte. Our description offers a new structurally based conceptual framework to evaluate functional data involving interactions between neurons and astrocytes in the mammalian brain.
Information mechanisms of the brain
- A N Radchenko
The logic of consciousness
- A Redozubov
Brain-machine interfaces: from macro-to microcircuits. In: Recent advances on the modular organization of the cortex
- M Lebedev
- I Opris
A framework for representing knowledge, MIT-AI Laboratory Memo 306. Massachusetts Institute of Technology A.I. Laboratory
- M Minsky
Spontaneous cortical activity in awake monkeys composed of neuronal avalanches
- T Petermanna
- Tc Thiagarajana
- Ma Lebedevb
- Mal Nicolelisb
- Dr Chialvoc
- D Plenz