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A comparison of (a) the original drawing of a cell assembly from Hebb's [74] typescript in which the cell assembly was first called a cage, and then changed to a lattice and (b) the published figure of a cell assembly from The Organization of Behavior [77], page 73. a Reprinted from an unpublished manuscript by Hebb, D. O. 1946, entitled "Carbon of most of the original MS of my book The Organization of Behavior (while the term "lattice" was still used instead of "cell assembly" [74]. The original is held in McGill University Archives, Montreal, Quebec, file MG1045. Reprinted with permission from Mary Ellen Hebb. b Reprinted from Hebb DO. The organization of behavior; a neuropsychological theory. NY: Wiley; 1949. [reprinted 2002 by Lawrence Erlbaum Associates, Mahwah, New Jersey]. Copyright (2002), with permission from Lawrence Erlbaum Associates

A comparison of (a) the original drawing of a cell assembly from Hebb's [74] typescript in which the cell assembly was first called a cage, and then changed to a lattice and (b) the published figure of a cell assembly from The Organization of Behavior [77], page 73. a Reprinted from an unpublished manuscript by Hebb, D. O. 1946, entitled "Carbon of most of the original MS of my book The Organization of Behavior (while the term "lattice" was still used instead of "cell assembly" [74]. The original is held in McGill University Archives, Montreal, Quebec, file MG1045. Reprinted with permission from Mary Ellen Hebb. b Reprinted from Hebb DO. The organization of behavior; a neuropsychological theory. NY: Wiley; 1949. [reprinted 2002 by Lawrence Erlbaum Associates, Mahwah, New Jersey]. Copyright (2002), with permission from Lawrence Erlbaum Associates

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The Organization of Behavior has played a significant part in the development of behavioural neuroscience for the last 70 years. This book introduced the concepts of the "Hebb synapse", the "Hebbian cell assembly" and the "Phase sequence". The most frequently cited of these is the Hebb synapse, but the cell assembly may be Hebb's most important con...

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... the typescript ( [74], page 69), the cell assembly was called a 'lattice', and the phrase "the specificity of such a lattice" was changed to "the specificity of such an assembly of cells" when the book was published ( [77], page 72). Figure 5 shows the original drawing of the lattice. In the original typescript there was no "phase sequence". ...

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... Research into NN has a lengthy history. Psychologist Donald Hebb [1] invented the oldest description of NN and created the Hebbian Learning scheme based on the brain plasticity process in the 1940s. In 1958, the perceptron, which is also known as the primary component of neural networks today and constructed as a two-layer neural network without a training method, was invented by Frank Rosenblatt [2]. ...
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The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance.
... Несколько позже был открыт и обратный процессдолговременная депрессия (ДВД), которая индуцируется низкочастотной стимуляцией синаптического входа [10][11][12]. ДВП и ДВД рассматриваются как формы так называемой хэббовской пластичности, молекулярные механизмы развития которой достаточно хорошо изучены [5,6,13,14]. Другим вариантом синаптической пластичности является гомеостатическая пластичность, которая служит для стабилизации активности нейронов и регуляции их активности по принципу отрицательной обратной связи [15][16][17][18]. ...
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Metaplasticity (plasticity of synaptic plasticity) is defined as a change in the direction or degree of synaptic plasticity in response to preceding neuronal activity. Recent advances in brain stimulation methods have enabled us to non-invasively examine cortical metaplasticity, including research in a clinical setting. According to current knowledge, non-invasive neuromodulation affects synaptic plasticity by inducing cortical processes that are similar to long-term potentiation and depression. Two stimulation blocks are usually used to assess metaplasticity priming and testing blocks. The technology of studying metaplasticity involves assessing the influence of priming on the testing protocol effect. Several dozen studies have examined the effects of different stimulation protocols in healthy persons. They found that priming can both enhance and weaken, or even change the direction of the testing protocol effect. The interaction between priming and testing stimulation depends on many factors: the direction of their effect, duration of the stimulation blocks, and the interval between them. Non-invasive brain stimulation can be used to assess aberrant metaplasticity in nervous system diseases, in order to develop new biomarkers. Metaplasticity disorders are found in focal hand dystonia, migraine with aura, multiple sclerosis, chronic disorders of consciousness, and age-related cognitive changes. The development of new, metaplasticity-based, optimized, combined stimulation protocols appears to be highly promising for use in therapeutic neuromodulation in clinical practice.
... An important factor to understand the association of particular neuron types and their cuasi-iterative prevalence is of course programmed embryogenesis (Li et al., 2016). In addition, there is the theoretical framework synthesized by Hebb (1949) and later modified with experiments from numerous groups (e.g., Frégnac, 2003;Malenka and Bear, 2004;Caporale and Dan, 2008;Baltaci et al., 2019;Brown and Donald, 2020;Magee and Grienberger, 2020). Although the generic name for these modules may be: "neuronal ensembles" (other names in: Carrillo-Reid and Yuste, 2020), their composition and complete numbers in different contexts is unknown. ...
... Countless experiments demonstrate diverse mechanisms for LTP and LTD (e.g., Stanton, 1996;Citri and Malenka, 2007;Kandel et al., 2014;Berry and Nedivi, 2016;Fauth and Tetzlaff, 2016;Abraham et al., 2019;Baltaci et al., 2019;Stampanoni-Bassi et al., 2019;Magee and Grienberger, 2020; Mateos-Aparicio and Rodríguez-Moreno, 2020), modifying the original Hebbian principle (Markram et al., 1997;Martin and Morris, 2002;Nicoll and Roche, 2013;Dringenberg, 2020). Whatever the mechanisms for generating LTP and LTD (Dudek and Bear, 1992;Malenka and Bear, 2004;Caporale and Dan, 2008;Hawes et al., 2013;Kandel et al., 2014;Nicoll, 2017;Diering and Huganir, 2018;Abraham et al., 2019;Brown and Donald, 2020), preferred paths for the flow of activity form stable circuits due to changes in synaptic weights. In turn, stable circuits encode memory traces (Kandel and Schwartz, 1982;Spatz, 1996;Sweatt, 2016;Andersen et al., 2017). ...
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Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.
... Coming from a neuroscientific perspective but with a deep understanding of psychology's historical roots, Hebb's (1949) landmark book, "Organization of behavior: A Neuropsychological Theory," played an important role in setting the stage for a revival of cognitivism. That such a revival may have been Hebb's intention is revealed by the recent discovery (Brown, 2020) that the original title of this book was "On Thought and Behavior." Indeed, in his preface Hebb asserted that "The failure of psychology to handle thought adequately...has been the essential weakness of modern psychological theory" (Hebb, 1949, p. xvi). ...
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Attention, the recruitment of processing resources, is viewed as pivotal for understanding normal behaviour and thought as well as the disorganizations associated with brain damage and disease. A brief history foreshadows aspects of a proposed taxonomy of attention that builds upon Posner's tripartite taxonomy. Posner's influential taxonomy views attention as a set of isolable neural systems (alerting, orienting and executive control), often working together to organize behaviour. For measuring the efficacy of these three networks, Posner and colleagues created the Attention Network Test (ANT). The impact of the taxonomy and this model task for exploring it is illustrated by the facts that they have spawned numerous variants designed for different purposes and that one or another variant has been used in almost a thousand publications. We have previously built upon this conceptual framework by considering: two modes of control over resource allocation which we labelled exogenous and endogenous and three domains over which these modes of control are presumed to operate (space, time and task or activity). The Combined Attention Systems Test (or CAST) was developed to measure the efficacy of the six kinds of attention implied by revised taxonomy. Lastly, this taxonomic effort is further developed by incorporating the distinction between overt, observable behaviour in the “real” world and covert “behaviour” in the realm of thought and imagination.
... In 1943, psychologist Warren McCulloch and mathematical logician Walter Pitts introduced the concept of artificial neural network and the mathematical model of artificial neuron, thus ushering in the era of artificial neural network research [5]. In 1949, psychologist Herb described the neuron learning rules [6]. In 1957, American neuroscientist Rosenblatt proposed a machine that can simulate human perception and called it "perceptron." ...
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The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination ( R 2 ) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone.
... If σ 0 , σ 1 and σ 2 are the only locations in the emerging affine map A (t 2 ), then σ 2 is adjacent to σ 0 and hence it must align with σ 0 along a certain η-direction η 20 (assuming a generic case, in which 1 and 2 are nonparallel, η 1 = ±η 2 ). Representing this alignment in the parahippocampal network, i.e., producing the corresponding imprints in the synaptic architecture via plasticity mechanisms (Leuner and Gould 2010;Caroni et al. 2012;Brown and O. 2020), requires actualization by igniting σ 2 and σ 0 consecutively during the activity of a particular η 20 . This can be achieved either by navigating between the corresponding σ -fields or off-line, via autonomous network activity. ...
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In the mammalian brain, many neuronal ensembles are involved in representing spatial structure of the environment. In particular, there exist cells that encode the animal’s location and cells that encode head direction. A number of studies have addressed properties of the spatial maps produced by these two populations of neurons, mainly by establishing correlations between their spiking parameters and geometric characteristics of the animal’s environments. The question remains however, how the brain may intrinsically combine the direction and the location information into a unified spatial framework that enables animals’ orientation. Below we propose a model of such a framework, using ideas and constructs from algebraic topology and synthetic affine geometry.
... Las bases del DUA se encuentran en los fundamentos neurocientíficos del aprendizaje, la psicología cognitiva y la psicología del desarrollo (Alba, 2016). Desde las teorías neurocognitivas de Donald Hebb, publicadas a partir de 1938, se comienza a considerar al aprendizaje como un mecanismo elemental de la plasticidad sináptica, por lo que activando redes ya existentes es mucho más probable que se conecte la nueva información (Brown, 2020). Estas redes se clasifican en afectivas, de reconocimiento y estratégicas; y no actuarían de manera secuenciada u ordenada, por lo que requieren de activación y estimulación permanente y simultánea. ...
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Resumen: El objetivo del estudio es caracterizar los elementos de una intervención pedagógica bajo los principios del DUA para el desarrollo de las habilidades psicolingüísticas en niños con TDL. El diseño de investigación corresponde al estudio de casos; la muestra, a profesoras de Educación Diferencial y psicopedagogas de la Región de Los Lagos-Chile; y el instrumento de recogida de datos es la entrevista semiestructurada. Los principales resultados reflejan la apropiación incipiente de prácticas de cooperación y pseudo-colaboración, y un bajo uso de estrategias de estimulación de las habilidades psicolingüísticas. Las conclusiones reflejan la necesidad de fortalecer los conocimientos teóricos y prácticos sobre las habilidades psicolingüísticas del alumnado, y sobre las dinámicas de intervención y colaboración profesional.
... Regarding the principle of associative relationship updating between neurons in human brain, Donald, a famous Canadian physiologist, proposed the Hebb learning rule [39]. He believes that the learning process of human brain neural network occurs at synapses between neurons, the strength of synaptic connections changes with the neuronal activity before and after synapses, and the amount of change is proportional to the total activities of two neurons. ...
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Uninterpretability has become the biggest obstacle to the wider application of deep neural network, especially in most human–machine interaction scenes. Inspired by the powerful associative computing ability of human brain neural system, a novel interpretable semantic representation model of noun context, associative knowledge network model, is proposed. The proposed network structure is composed of only pure associative relationships without relation label and is dynamically generated by analysing neighbour relationships between noun words in text, in which incremental updating and reduction reconstruction strategies can be naturally introduced. Furthermore, a novel interpretable method is designed for the practical problem of checking the semantic coherence of noun context. In proposed method, the associative knowledge network learned from the text corpus is first regarded as a background knowledge network, and then the multilevel contextual associative coupling degree features of noun words in given detection document are computed. Finally, contextual coherence detection and the location of those inconsistent noun words can be realized by using an interpretable classification method such as decision tree. Our sufficient experimental results show that above proposed method can obtain excellent performance and completely reach or even partially exceed the performance obtained by the latest deep neural network methods especially in F1 score metric. In addition, the natural interpretability and incremental learning ability of our proposed method should be extremely valuable than deep neural network methods. So, this study provides a very enlightening idea for developing interpretable machine learning methods, especially for the tasks of text semantic representation and writing error detection.
... Las bases del DUA se encuentran en los fundamentos neurocientíficos del aprendizaje, la psicología cognitiva y la psicología del desarrollo (Alba, 2016). Desde las teorías neurocognitivas de Donald Hebb, publicadas a partir de 1938, se comienza a considerar al aprendizaje como un mecanismo elemental de la plasticidad sináptica, por lo que activando redes ya existentes es mucho más probable que se conecte la nueva información (Brown, 2020). Estas redes se clasifican en afectivas, de reconocimiento y estratégicas; y no actuarían de manera secuenciada u ordenada, por lo que requieren de activación y estimulación permanente y simultánea. ...
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Este trabajo describe una experiencia exploratoria sobre la implementación de una metodología de enseñanza-aprendizaje —y sus ideas constituyentes— para la Formación Inicial Docente, la cual pretende promover el desarrollo pre-profesional en futuros profesores de matemáticas a través de experiencias concretas que promuevan su desarrollo pre-profesional a través de ciclos de planificación, implementación y evaluación de objetivos de aprendizaje establecidos en el currículum nacional chileno en la asignatura de matemáticas. Para este efecto, los futuros profesores articulan y orquestan, sistemáticamente, diversas secuencias didácticas y formas de instrucción y enseñanza —basadas tanto en teoría educacional como en resultados de investigaciones empíricas— en un contexto real de clases con estudiantes de Enseñanza Media y en estrecha cooperación con un profesor en ejercicio de un establecimiento educacional y un docente universitario. Aspectos teóricos de esta metodología están adaptados del Estudio de Clases Japonés, el cual rescata, analiza y utiliza buenas prácticas de profesores exhibidas en clases hace más de un siglo en Japón y son la forma dominante de práctica y desarrollo profesional docente en aquel archipiélago. El resultado de la experiencia aporta evidencias que indican que el uso de esta metodología es una estrategia poderosa para mejorar la Formación Inicial Docente.
... En 1959, el neuropsicólogo Donald Hebb propuso la teoría de la "asamblea celular", actualmente conocida como la teoría de Hebb o hebbiana del aprendizaje, que puede resumirse en su frase célebre "Neurons that fire together, wire together" o, traducida al español, "Las neuronas que se activan juntas refuerzan su conexión". Esta teoría habla de cómo se consolida el conocimiento con base a las sinapsis o circuitos neuroanatómicos preexistentes y cómo la repetición de una acción tiene gran peso para generar memoria a largo plazo (Brown, 2020). Los músicos crean redes neuronales con la activación simultánea de la corteza auditiva y la corteza motora al realizar movimientos con sus manos para producir sonido. ...
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Introducción: A nivel neurofisiológico el cerebelo, los ganglios basales y el sistema límbico son importantes en la coordinación y memoria del movimiento. Objetivo: Comprender los procesos que intervienen en la relación sensoperceptiva de la audición y el aprendizaje motor es una motivación permanente de diferentes disciplinas. Método: Se presenta una revisión documental que tuvo por objetivo analizar la relación de la percepción auditiva y el aprendizaje motor utilizando análisis de contenido desde las perspectivas de audiología, medicina y neurorrehabilitación. Las palabras clave y combinaciones que se tuvieron en cuenta fueron: percepción auditiva, aprendizaje, equilibrio, coordinación y las combinaciones audición-aprendizaje, audición-equilibrio, y audición-coordinación. Se utilizaron las bases de datos y metabuscadores Pubmed, Medscape, Trip, ScienceDirect, EBSCOhots, Pedro, Scielo, y Lilacs. Bibliotecas virtuales como SINAB, Cochrane, Universidad de Málaga, UsNational Library of Medicine, y National Institutes of Health. Se seleccionaron 22 artículos que cumplieron con los criterios de inclusión. Resultados: Se encontró relación entre la percepción auditiva y el aprendizaje motor en la comunicación de la información sensorial auditiva y motora a nivel del procesamiento en el cerebelo y ganglios, que es una parte fundamental en la retención y transferencia motriz. Conclusión: En el proceso del aprendizaje motor que involucra la experiencia del movimiento, proponemos la participación de la audición, mediante integrar las señales percibidas-visuales, auditivas, motrices y vestibulares- que se concretan en mejorar el aprendizaje, haciéndolo más eficaz y generando una memoria más duradera.