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Title: Neuroscience of Learning: Hebb's Theory


1
PSYCHOLOGY OF LEARNING
NEUROSCIENCE OF LEARNING HEBBS THEORY
HEBB'S THEORY
NEUROSCIENCE OF LEARNING
RACHEL HONG
1
2
  • Donald O. Hebbs Theory of Learning and Memory
  • Trettenbreins Critiques of the Neurophysiologic
    Explanation of Learning and Memory
  • Resolution of Recent Critiques Using Modern
    Neurophysiological Research
  • Reconciliation of the Differences Between
    Physiologists and Psychologists About the Role of
    Synaptic Plasticity in Learning and Memory
  • Synaptic Change and the Formation of Cell
    Assemblies are Fundamental for Theories of Memory
  • Cell Assemblies Have Been Verified by
    Neuroimaging
  • Hebbs Synaptic Learning Rule and Cell Assembly
    Theory are Used in Computational Neuroscience and
    Robotics
  • Abnormalities in Synaptic Plasticity Underlie
    Cognitive and Motor Dysfunctions Pain Mechanisms
    and Drug Addiction

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
2
3
  • There is a considerable literature
  • on the neurobiology of learning and memory
  • that shows the importance of synaptic plasticity
  • as the first step in the chain of cellular and
  • biochemical events involved in memory formation
  • Once memories are formed, synaptic modification
    is essential for their expression (Langille
    Brown, 2018).
  • The discussion will be in terms of Hebbs (1949)
    neuropsychological theory.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
3
4
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
DONALD O. HEBBS (1904 - 1985) THEORY OF LEARNING
AND MEMORY
NEURONS THAT FIRE TOGETHER WIRE TOGETHER.
4
5
HEBB'S THEORY
  • Hebbs (1949) theory assumed that the
    neurophysiological changes underlying learning
    and memory occur in three stages
  • (1) synaptic changes
  • (2) formation of a cell assembly
  • (3) formation of a phase sequence
  • which link the neurophysiological changes
    underlying learning and memory as studied by
    physiologists to the study of thought, and mind
    as conceived by cognitive psychologists.
  • Hebbs neurophysiological assumption (Hebb, 1949)
    states that
  • When an axon of cell A is near enough to excite
    a cell B and repeatedly or persistently takes
    part in firing it, some growth process or
    metabolic change takes place in one or both cells
    such that As efficiency, as one of the cells
    firing B, is increased.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
5
6
HEBB'S THEORY
  • The cell assembly is a set of neurons and the
    pathways connecting them, which act together
    (Hebb, 1949),
  • such that a stimulus activating pathway 1 will
    activate a reverberating circuit of N pathways
    (in Hebbs example, n 15).
  • It is a hypothetical reverberating system,
    proposed as a mediating process, an element of
    thought, capable of holding an excitation and
    bridging a gap in time between stimulus and
    response (Hebb, 1972).

CELL ASSEMBLY
13
12
1, 4
5, 9
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
6, 10
3, 11
2, 14
8
7, 15
6
7
HEBB'S THEORY
  • A series of cell assemblies, connected by neural
    activity over time is a Phase Sequence, which
    provides the neural basis for a train of
    thought from one cell assembly to another (Hebb,
    1949).
  • The cell assembly relates the individual nerve
    cell to psychological phenomenon
  • such that a bridge has been thrown across the
    great gap between the details of neurophysiology
    and the molar conceptions of psychology (Hebb,
    1949).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
PHASE SEQUENCE
7
8
HEBB'S THEORY
  • Hebb elaborated on
  • How this theory could account for learning and
    memory
  • How new learning could be associated with
    previous learning, and
  • How quick learning (similar to the single trial
    learning of Gallistel and Balsam (2014)) might
    occur (Hebb, 1949).
  • Hebbs cell assembly theory showed
  • how differences between psychologists and
    physiologists,
  • who use different definitions for the same
    phenomena,
  • could be reconciled into a theory of the
    neurophysiological basis of learning and memory.
  • Hebbs assumption contains two concepts synaptic
    plasticity and some growth process or metabolic
    change in the neuron, which is intrinsic
    plasticity (Titley, Brunel, Hansel, 2017).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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9
  • The only theory to realistically deal with
    problems of behavior, thought process and
    learning
  • Theory has defects, but no real competitors
  • It is criticized, because it is difficult to
    experimentally prove

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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TRETTENBREINS CRITIQUES OF THE NEUROPHYSIOLOGIC
EXPLANATION OF LEARNING AND MEMORY
  • The synapse-centered view of learning and memory
    is not focused and that the neurobiological basis
    of learning and memory is still unclear (Langille
    Brown, 2018).
  • Trettenbrein (2016) argues that the concept of
    the synapse as the locus of memory is not
    sensible and that a paradigm shift is necessary.
  • However, no new paradigm is provided, but he
    suggests that the memory mechanism is (sub-)
    molecular in nature.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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11
TRETTENBREINS CRITIQUES
  • There are six critiques of the synaptic
    plasticity theory of memory in Trettenbrein
    (2016)s article
  • (1) The synapse may not be the sole locus of
    learning and memory
  • (2) A synaptic locus of memory does not fit well
    with philosophical and cognitive theories of
    learning and memory
  • (3) Memories survive despite synapse destruction
    and synaptic and (or) protein turn-over
  • (4) Evidence from spatial training suggests that
    there is a need to separate learning from memory
  • (5) Existing learning mechanisms cannot explain
    information that is encoded in a single trial
    (Gallistel and Balsam, 2014)
  • (6) Memory may be sub-cellular in nature

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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RESOLUTION OF RECENT CRITIQUES
USING MODERN NEUROPHYSIOLOGICAL RESEARCH
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
12
13
TRETTENBREINS CRITIQUES
  • The critique of synaptic plasticity theory
    proposed by Trettenbrein (2016)
  • can be resolved using Hebbs synaptic theory,
  • research based on cell assemblies as components
    of neural networks,
  • and current research on the cellular and
    molecular basis of memory formation
  • to show the nature of synaptic plasticity in
    understanding the neurobiology of learning and
    memory.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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14
RECONCILIATION OF THE DIFFERENCES BETWEEN
PHYSIOLOGISTS AND PSYCHOLOGISTS ABOUT THE ROLE OF
SYNAPTIC PLASTICITY IN LEARNING AND MEMORY
  • Critique of Trettenbrein (2016) focuses on
    demise (death, downfall, disappearance or final
    fate) of the synaptic theory of memory.
  • The synaptic theory of memory has not
    disappeared, but that there are two components of
    this theory synaptic plasticity and
    intra-cellular biochemical changes.
  • The concern is whether memory consists of the
    synaptic changes activated by intracellular
    biochemical changes OR the intracellular
    biochemical changes expressed via synaptic
    plasticity (Langille Brown, 2018).
  • Memory, as conceived by Hebb, consists
    inseparably of both synaptic plasticity and
    intrinsic plasticity of the neurons (Lisman,
    Cooper, Sehgal, Silva, 2018).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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15
SYNAPTIC CHANGE AND THE FORMATION OF CELL
ASSEMBLIES ARE FUNDAMENTAL FOR THEORIES OF MEMORY
  • Hebb (1949, 1959) realized that his theory would
    need revision for new discoveries.
  • His ideas on synaptic plasticity (Favero,
    Cangiano, Busetto, 2014), cell assemblies
    (Wallace Kerr, 2010) and phase sequences
    (Almeida-Filho et al., 2014) continue to
    stimulate new research and discussion is a
    tribute to his prescience.
  • Physiological mechanisms of learning and memory
    (Johansen et al., 2014)
  • Learning and development (Munakata and Pfaffly,
    2004)
  • Memory span (Oberauer, Jones, Lewandowsky,
    2015)
  • Decision making (Wang, 2012)
  • Language learning (Wennekers, Garagnani,
    Pulvermüller, 2006).
  • Posner and Rothbart (2004, 2007) suggested that
    using his ideas to integrate the disparate
    branches of Psychology and Neuroscience.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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CELL ASSEMBLIES HAVE BEEN VERIFIED BY NEUROIMAGING
  • Hebbs theories on the neurophysiological basis
    of learning and memory integrate synaptic
    neurophysiology with psychological concepts like
    attention, perception, thought and mindthe
    concepts which Pavlov avoided in his objective
    approach to memory.
  • Hebbs theory effectively integrated Pavlovs
    concepts of the physiology of learning with
    Lashleys (1932) criticism that Pavlov ignored
    psychological concepts.
  • Neuroimaging studies have shown the usefulness of
    Hebbs ideas for understanding both the
    psychological and physiological mechanisms of
    memory.
  • Memory processes have been shown by fMRI and
    other neuroimaging methods to be distributed
    across many cortical areas (Miyamoto, Osada,
    Adachi, 2014).
  • Christophel, Klink, Spitzer, Roelfsema, Haynes
    (2017) showed that different cortical neural
    networks are activated in different types of
    working memory.
  • ONeil et al. (2012) found that different
    cortical regions were activated in recognition
    memory.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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THE IMPORTANCE OF HEBBS IDEAS
  • There is still existence on the Hebb synapse
    (Brown, 2020).
  • To name a few
  • Graham Collingridges paper on Hebb synapses and
    beyond
  • Ole Paulsons paper on Neuromodulation of Hebbian
    synapses
  • Zahid Padamseys presentation on a new framework
    for Hebbian plasticity in the hippocampus.
  • For understanding cognitive (Takamiya, Yuki,
    Hirokawa, Manabe, Sakurai, 2019)
  • The focus on synaptic mechanisms is in learning
    and memory.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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USES OF HEBBS WORK TODAY
  • Learning and memory
  • Long-term effects of the environment on
    development
  • Aging
  • Neurocomputing
  • Artificial intelligence
  • Robotics

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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NEUROPHYSIOLOGY OF LEARNING AND MEMORY
  • The proposal that long-term potentiation was a
    synaptic model of memory (Bliss, Collingridge,
    1993)
  • led to a number of examinations of Hebbs concept
    of synaptic plasticity (Sweatt, 2016).
  • The concept of spike timing dependent plasticity
    (STDP) is built on the concept of the Hebb
    synapse, producing the term Hebbian STDP
    (Brzosko, Mierau, Paulsen, 2019).
  • The Dynamic Hebbian Learning Model (dynHebb) is
    developed to support for the complexities of STDP
    (Olde Scheper, Meredith, Mansvelder, van Pelt,
    van Ooyen, 2018).
  • McNaughton (2003) wrote about how Hebbs theory
    stimulated his research on long-term potentiation
    and memory.
  • Andersen, Krauth and Nabavi (2017) stated that
    Hebbian plasticity, as represented by long-term
    potentiation and long-term depression of
    synapses, is the most influential hypothesis to
    support for encoding of memories.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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CELL ASSEMBLY
  • Cell assembly has led to new research on neural
    networks (Li, Liu, Tsien, 2016) and the
    molecular mechanisms underlying the cell assembly
    (Pulvermüller, Garagnani, Wennekers, 2014).
  • Harris (2012) stated that One of the most
    influential theories for cortical function is the
    cell assembly hypothesis first proposed over
    half a century ago (Hebb, 1949).
  • Harris (2005) proposed four experimental tests
    for the temporal organization of cell assemblies.
  • Eichenbaum (2018) proposed that cell assemblies
  • be studied as units of information processing
    to guide research on
  • the structure and organization of neural
    representations in perception and cognition.

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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CELL ASSEMBLY
  • Buzsaki (2010) defined the cell assembly as the
    neural syntax of the brain and
  • suggested ways in which the neural organization
    of cell assemblies could be understood in the
    context of both brain function and brain-machine
    interfaces.
  • He proposed that cell assemblies were linked by
    dynamically changing constellations of synaptic
    weights which he called synapsembles and
  • suggested that the objective identification of
    the cell assembly
  • requires a temporal framework and a reader
    mechanism
  • which can integrate the activity of cell
    assemblies over time.
  • The result has led to the consideration of
    Hebbian cell assemblies as the basis for
    semantic circuits
  • which define the cortical locus of semantic
    knowledge and
  • to the development of neurocomputational models
    of brain function (Tomasello, Garagnani,
    Wennekers, Pulvermüller, 2018).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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PHASE SEQUENCES
  • Hebbs concept of phase sequences as synchronized
    sets of cell assemblies
  • has been examined by recording action potentials
  • from the hippocampus and cortex of actively
    behaving rats (Almeida-Filho, et al., 2014).
  • The results suggest that the cell assemblies are
    the building blocks of neural representations,
  • while the phase sequences that link cell
    assemblies are modifiable by new experiences,
    modulating the neural connections of cognition
    and behaviour.
  • This approach has been used to apply Hebbian
    learning and cell assemblies
  • to the construction of neurocomputational models
    of language learning which simulate the brain
    mechanisms of word meaning in semantic hubs
    (Tomasello, et al., 2018).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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NEUROCOMPUTING
  • The concept of Hebbian learning is used in
  • neurocomputing and the development of artificial
    neural networks (Kuriscak, Marsalek, Stroffek,
    Toth, 2015).
  • The mathematical definition of the change in
    activity at a Hebb synapse through synaptic
    scaling
  • has allowed for the quantitative definition of a
    Hebbian Cell Assembly (Tetzlaff, Dasgupta,
    Kulvicius, Wörgötter, 2015) for use in robotics
    and artificial intelligence.
  • Virtual Cell Assembly Robots (CABots)
  • have been built using cell assemblies as the
    basis of short- and long-term artificial memories
    (Huyck, Mitchell, 2018) and
  • the cell assembly has been proposed as the basis
    for computer simulation of human brain function
    (Huyck, 2019).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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HEBBS SYNAPTIC LEARNING RULE AND CELL ASSEMBLY
THEORY ARE USED IN COMPUTATIONAL NEUROSCIENCE AND
ROBOTICS
  • Cell assemblies and phase sequences are used to
    develop
  • theories of the cortical control of behavior
    (Palm, Knoblauch, Hauser, Schüz, 2014)
  • network theories of memory (Fuster, 1997) and
  • computer models of memory processes (Lansner,
    2009).
  • Driven by neurophysiological and biophysical
    findings, they concern the basic neuronal
    mechanisms and the detailed temporal processes of
    neuronal activation and interaction, and by
    computational arguments and requirements.
  • Cell assembly theory has helped in developing the
    anatomical features that underlie the location of
    memory storage in the cortex (Palm et al., 2014).
  • Hebbian learning rules and cell assemblies
  • Are applied in computer models of the brain to
    build neural networks based on STDP (Markram,
    Gerstner, W., Sjöström, 2011).
  • Are currently used in robotics (Calderon, Baidyk,
    Kussul, 2013).
  • Hebbian learning rules are used to control
    brain-robot interfaces in neurorehabilitation
    (Takeuchi Izumi, 2015).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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ABNORMALITIES IN SYNAPTIC PLASTICITY UNDERLIE
COGNITIVE AND MOTOR DYSFUNCTIONS PAIN MECHANISMS
AND DRUG ADDICTION
  • The activation of the network of synaptic
    connections in a cell assembly
  • requires changes in synaptic strength
  • to establish the connectivity of the neurons in
    the cell assembly.
  • Cell assemblies are a collection of activated
    synapses and the sufficiently strong activation
    of these synapses
  • causes biochemical changes in the neurons of the
    cell assembly.
  • Biochemical changes and gene activation within
    the neurons of a cell assembly are required to
    maintain memories (Li, Liu, Tsien, 2016).
  • These involve complex interactions between
    excitatory and inhibitory synapses (Barron,
    Vogels, Behrens, Ramaswami, 2017).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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  • The biochemical changes in the neurons of a cell
    assembly that are activated by transient changes
    in synaptic activity involve epigenetic
    mechanisms including chromatin remodeling
  • which drives changes in the transcription and
    translation of information in the DNA, protein
    synthesis and cellular changes underlying
    learning and memory formation (Vogel-Ciernia
    Wood, 2014).
  • Hebb (1949) stated that the synaptic changes
    following repeated stimulation at a synapse lead
    to some growth process or metabolic change in
    one or both cells such that As efficiency, as
    one of the cells firing B, is increased.
  • Neuroscientific research on the cellular and
    molecular basis of memory in the last 70 years
    has been finding these growth processes and
    metabolic changes that underlie memory (Poo et
    al., 2016).
  • Synaptic change is not limited to learning and
    memory, but forms the basis of neural changes in
    perception (Yang, Weiner, Zhang, Cho, Bao
    2011), pain (Luo, Kuner, Kuner, 2014) and drug
    addiction (Lüscher, 2013).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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  • Neurological disorders which involve cognitive or
    motor dysfunction are the result of synaptic
    abnormalities (Kouroupi et al., 2017).
  • Synaptic dysfunction underlies
  • neurodevelopmental disorders like autism, Rett
    syndrome, Down syndrome and ADHD (Moretto, Murru,
    Martano, Sassone, Passafaro, 2018) and
  • neurological disorders of adulthood and aging,
    including Alzheimer disease, Parkinsons disease,
    Huntingtons disease and multiple sclerosis
    (Torres, Vallejo, Inestrosa, 2017)
  • Impaired hippocampal long-term potentiation and
    consolidation
  • may struggle in forming new, lasting memories
    (Weintraub, Wicklund, Salmon, 2012), termed
    anterograde amnesia.
  • The decreases in synaptic strength (and removal
    of the physical substrates of memories) and
    synapse loss
  • may erase the past memories in retrograde
    amnesia,
  • Both of which are characteristic of Alzheimers
    disease (Beatty, Salmon, Butters, Heindel,
    Granholm, 1988).

NEUROSCIENCE OF LEARNING
HEBB'S THEORY
  • A synaptic plasticity theory of memory can
    demonstrate the memory impairments in
    neuropathologic conditions like Alzheimers
    disease.

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NEUROSCIENCE OF LEARNING
HEBB'S THEORY
- REMEMBER -
Practice Makes Perfect!
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REFERENCES
Almeida-Filho, D. G., Lopes-dos-Santos, V.,
Vasconcelos, N. A., Miranda, J. G., Tort, A. B.,
Ribeiro, S. (2014). An investigation of Hebbian
phase sequences as assembly graphs. Frontiers in
neural circuits, 8, 34. doi10.3389/fncir.2014.000
34 Andersen, N., Krauth, N., Nabavi, S. (2017).
Hebbian plasticity in vivo relevance and
induction. Current opinion in neurobiology, 45,
188192. doi10.1016/j.conb.2017.06.001 Barron,
H. C., Vogels, T. P., Behrens, T. E.,
Ramaswami, M. (2017). Inhibitory engrams in
perception and memory. Proceedings of the
National Academy of Sciences of the United States
of America, 114(26), 66666674.
doi10.1073/pnas.1701812114 Beatty, W. W.,
Salmon, D. P., Butters, N., Heindel, W. C.,
Granholm, E. L. (1988). Retrograde amnesia in
patients with Alzheimer's disease or Huntington's
disease. Neurobiology of aging, 9(2), 181186.
doi10.1016/s0197-4580(88)80048-4 Bliss, T. V.,
Collingridge, G. L. (1993). A synaptic model of
memory long-term potentiation in the
hippocampus. Nature, 361(6407), 3139.
doi10.1038/361031a0 Brown, R. E. (2020). Donald
O. Hebb and the Organization of Behavior 17
years in the writing. Mol Brain 13, 55.
doi.org/10.1186/s13041-020-00567-8
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
29
30
REFERENCES
Brzosko, Z., Mierau, S. B., Paulsen, O. (2019).
Neuromodulation of Spike-Timing-Dependent
Plasticity Past, Present, and Future. Neuron,
103(4), 563581. doi10.1016/j.neuron.2019.05.041
Buzsáki G. (2010). Neural syntax cell
assemblies, synapsembles, and readers. Neuron,
68(3), 362385. doi10.1016/j.neuron.2010.09.023 C
alderon, D., Baidyk, T., Kussul, E. (2013).
Hebbian ensemble neural network for robot
movement control. Optical Memory and Neural
Networks, 22(3), 166183. doi10.3103/s1060992x130
30028 Christophel, T. B., Klink, P. C., Spitzer,
B., Roelfsema, P. R., Haynes, J. D. (2017). The
Distributed Nature of Working Memory. Trends in
cognitive sciences, 21(2), 111124.
doi10.1016/j.tics.2016.12.007 Eichenbaum H.
(2018). Barlow versus Hebb When is it time to
abandon the notion of feature detectors and adopt
the cell assembly as the unit of cognition?.
Neuroscience letters, 680, 8893.
doi10.1016/j.neulet.2017.04.006 Favero, M.,
Cangiano, A., Busetto, G. (2014). Hebb-based
rules of neural plasticity are they ubiquitously
important for the refinement of synaptic
connections in development?. The Neuroscientist
a review journal bringing neurobiology, neurology
and psychiatry, 20(1), 814. doi10.1177/107385841
3491148
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
30
31
REFERENCES
Fuster J. M. (1997). Network memory. Trends in
neurosciences, 20(10), 451459.
doi10.1016/s0166-2236(97)01128-4 Gallistel C.
R., Balsam P. D. (2014). Time to rethink the
neural mechanisms of learning and memory.
Neurobiology of learning and memory, 108,
136144. doi10.1016/j.nlm.2013.11.019 Harris K.
D. (2005). Neural signatures of cell assembly
organization. Nature reviews. Neuroscience, 6(5),
399407. doi10.1038/nrn1669 Harris K. D. (2012).
Cell assemblies of the superficial cortex.
Neuron, 76(2), 263265. doi10.1016/j.neuron.2012.
10.007 Hebb D. O. (1949). The Organisation of
Behaviour. New York, NY John Wiley Sons. Hebb
D. O. (1959). A neuropsychological theory, in
Psychology A Study of A Science, (Vol. 1) ed.
Koch S. (New York,NY McGraw Hill ), 622643.
Hebb D.O. (1972). Textbook of Psychology.
Philadelphia, PA Saunders. Huyck, C.,
Mitchell, I. (2018). CABots and Other Neural
Agents. Frontiers in neurorobotics, 12, 79.
doi10.3389/fnbot.2018.00079 Huyck, C. R. (2019).
A Neural Cognitive Architecture. Cognitive
Systems Research. doi10.1016/j.cogsys.2019.09.023
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
31
32
REFERENCES
Johansen, J. P., Diaz-Mataix, L., Hamanaka, H.,
Ozawa, T., Ycu, E., Koivumaa, J., Kumar, A., Hou,
M., Deisseroth, K., Boyden, E. S., LeDoux, J.
E. (2014). Hebbian and neuromodulatory mechanisms
interact to trigger associative memory formation.
Proceedings of the National Academy of Sciences
of the United States of America, 111(51),
E5584E5592. doi10.1073/pnas.1421304111 Kouroupi,
G., Taoufik, E., Vlachos, I. S., Tsioras, K.,
Antoniou, N., Papastefanaki, F., Chroni-Tzartou,
D., Wrasidlo, W., Bohl, D., Stellas, D., Politis,
P. K., Vekrellis, K., Papadimitriou, D.,
Stefanis, L., Bregestovski, P., Hatzigeorgiou, A.
G., Masliah, E., Matsas, R. (2017). Defective
synaptic connectivity and axonal neuropathology
in a human iPSC-based model of familial
Parkinson's disease. Proceedings of the National
Academy of Sciences of the United States of
America, 114(18), E3679E3688. doi10.1073/pnas.16
17259114 Kuriscak, E., Marsalek, P., Stroffek,
J., Toth, P. G. (2015). Biological context of
Hebb learning in artificial neural networks, a
review. Neurocomputing, 152, 2735.
doi10.1016/j.neucom.2014.11.022 Langille, J. J.,
Brown, R. E. (2018). The Synaptic Theory of
Memory A Historical Survey and Reconciliation of
Recent Opposition. Frontiers in systems
neuroscience, 12, 52. doi.org/10.3389/fnsys.2018.0
0052
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
32
33
REFERENCES
Lansner A. (2009). Associative memory models
from the cell-assembly theory to biophysically
detailed cortex simulations. Trends in
neurosciences, 32(3), 178186. doi10.1016/j.tins.
2008.12.002 Lashley, K. S. (1932). Studies of
cerebral function in learning. VIII. A reanalysis
of data on mass action in the visual cortex. The
Journal of Comparative Neurology, 54(1), 7784.
doi10.1002/cne.900540106 Li, M., Liu, J.,
Tsien, J. Z. (2016). Theory of Connectivity
Nature and Nurture of Cell Assemblies and
Cognitive Computation. Frontiers in neural
circuits, 10, 34. doi10.3389/fncir.2016.00034 Lis
man, J., Cooper, K., Sehgal, M., Silva, A. J.
(2018). Memory formation depends on both
synapse-specific modifications of synaptic
strength and cell-specific increases in
excitability. Nature neuroscience, 21(3),
309314. doi10.1038/s41593-018-0076-6 Luo, C.,
Kuner, T., Kuner, R. (2014). Synaptic
plasticity in pathological pain. Trends in
neurosciences, 37(6), 343355. doi10.1016/j.tins.
2014.04.002 Lüscher C. (2013). Drug-evoked
synaptic plasticity causing addictive behavior.
The Journal of neuroscience the official
journal of the Society for Neuroscience, 33(45),
1764117646. doi10.1523/JNEUROSCI.3406-13.2013
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
33
34
REFERENCES
Markram, H., Gerstner, W., Sjöström, P. J.
(2011). A history of spike-timing-dependent
plasticity. Frontiers in synaptic neuroscience,
3, 4. doi10.3389/fnsyn.2011.00004 McNaughton, B.
L. (2003). Long-term potentiation, cooperativity
and Hebbs cell assemblies a personal history.
Philosophical Transactions of the Royal Society
B Biological Sciences, 358(1432), 629634.
doi10.1098/rstb.2002.1231 Miyamoto, K., Osada,
T., Adachi, Y. (2014). Remapping of memory
encoding and retrieval networks insights from
neuroimaging in primates. Behavioural brain
research, 275, 5361. doi10.1016/j.bbr.2014.08.04
6 Moretto, E., Murru, L., Martano, G., Sassone,
J., Passafaro, M. (2018). Glutamatergic
synapses in neurodevelopmental disorders.
Progress in neuro-psychopharmacology biological
psychiatry, 84(Pt B), 328342. doi10.1016/j.pnpbp
.2017.09.014 Munakata, Y., Pfaffly, J. (2004).
Hebbian learning and development. Developmental
science, 7(2), 141148. doi10.1111/j.1467-7687.20
04.00331.x Olde Scheper, T. V., Meredith, R. M.,
Mansvelder, H. D., van Pelt, J., van Ooyen, A.
(2018). Dynamic Hebbian Cross-Correlation
Learning Resolves the Spike Timing Dependent
Plasticity Conundrum. Frontiers in Computational
Neuroscience, 11. doi10.3389/fncom.2017.00119
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
34
35
REFERENCES
O'Neil, E. B., Protzner, A. B., McCormick, C.,
McLean, D. A., Poppenk, J., Cate, A. D.,
Köhler, S. (2012). Distinct patterns of
functional and effective connectivity between
perirhinal cortex and other cortical regions in
recognition memory and perceptual discrimination.
Cerebral cortex (New York, N.Y. 1991), 22(1),
7485. doi10.1093/cercor/bhr075 Oberauer, K.,
Jones, T., Lewandowsky, S. (2015). The Hebb
repetition effect in simple and complex memory
span. Memory cognition, 43(6), 852865.
doi10.3758/s13421-015-0512-8 Palm, G.,
Knoblauch, A., Hauser, F., Schüz, A. (2014).
Cell assemblies in the cerebral cortex.
Biological cybernetics, 108(5), 559572.
doi10.1007/s00422-014-0596-4 Poo, M. M.,
Pignatelli, M., Ryan, T. J., Tonegawa, S.,
Bonhoeffer, T., Martin, K. C., Rudenko, A., Tsai,
L. H., Tsien, R. W., Fishell, G., Mullins, C.,
Gonçalves, J. T., Shtrahman, M., Johnston, S. T.,
Gage, F. H., Dan, Y., Long, J., Buzsáki, G.,
Stevens, C. (2016). What is memory? The present
state of the engram. BMC biology, 14, 40.
doi10.1186/s12915-016-0261-6 Posner, M. I.,
Rothbart, M. K. (2004). Hebb's Neural Networks
Support the Integration of Psychological Science.
Canadian Psychology/Psychologie canadienne,
45(4), 265278. doi10.1037/h0086997
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
35
36
REFERENCES
Posner, M. I., Rothbart, M. K. (2007). Research
on attention networks as a model for the
integration of psychological science. Annual
review of psychology, 58, 123.
doi10.1146/annurev.psych.58.110405.085516 Pulverm
üller, F., Garagnani, M., Wennekers, T. (2014).
Thinking in circuits toward neurobiological
explanation in cognitive neuroscience. Biological
cybernetics, 108(5), 573593. doi10.1007/s00422-0
14-0603-9 Sweatt J. D. (2016). Neural plasticity
and behavior - sixty years of conceptual
advances. Journal of neurochemistry, 139 Suppl 2,
179199. doi10.1111/jnc.13580 Takamiya, S.,
Yuki, S., Hirokawa, J., Manabe, H., Sakurai, Y.
(2019). Dynamics of memory engrams. Neuroscience
Research. doi10.1016/j.neures.2019.03.005 Takeuch
i, N., Izumi, S. (2015). Combinations of stroke
neurorehabilitation to facilitate motor recovery
perspectives on Hebbian plasticity and
homeostatic metaplasticity. Frontiers in human
neuroscience, 9, 349. doi10.3389/fnhum.2015.00349
Tetzlaff, C., Dasgupta, S., Kulvicius, T.,
Wörgötter, F. (2015). The Use of Hebbian Cell
Assemblies for Nonlinear Computation. Scientific
reports, 5, 12866. doi10.1038/srep12866
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
36
37
REFERENCES
Titley, H. K., Brunel, N., Hansel, C. (2017).
Toward a Neurocentric View of Learning. Neuron,
95(1), 1932. doi10.1016/j.neuron.2017.05.021 Tom
asello, R., Garagnani, M., Wennekers, T.,
Pulvermüller, F. (2018). A Neurobiologically
Constrained Cortex Model of Semantic Grounding
With Spiking Neurons and Brain-Like Connectivity.
Frontiers in computational neuroscience, 12, 88.
doi10.3389/fncom.2018.00088 Torres, V. I.,
Vallejo, D., Inestrosa, N. C. (2017). Emerging
Synaptic Molecules as Candidates in the Etiology
of Neurological Disorders. Neural plasticity,
2017, 8081758. doi10.1155/2017/8081758 Trettenbre
in P.C. (2016). The demise of the synapse as the
locus of memory a looming paradigm shift?.
Frontiers in systems neuroscience 10, 88.
doi10.3389/fnsys.2016.00088 Vogel-Ciernia, A.,
Wood, M. A. (2014). Neuron-specific chromatin
remodeling a missing link in epigenetic
mechanisms underlying synaptic plasticity,
memory, and intellectual disability disorders.
Neuropharmacology, 80, 1827. doi10.1016/j.neurop
harm.2013.10.002
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
37
38
REFERENCES
Wallace, D. J., Kerr, J. N. (2010). Chasing the
cell assembly. Current opinion in neurobiology,
20(3), 296305. Wang X. J. (2012). Neural
dynamics and circuit mechanisms of
decision-making. Current opinion in neurobiology,
22(6), 10391046. doi10.1016/j.conb.2012.08.006 W
eintraub, S., Wicklund, A. H., Salmon, D. P.
(2012). The neuropsychological profile of
Alzheimer disease. Cold Spring Harbor
perspectives in medicine, 2(4), a006171.
doi10.1101/cshperspect.a006171 Wennekers, T.,
Garagnani, M., Pulvermüller, F. (2006).
Language models based on Hebbian cell assemblies.
Journal of physiology, Paris, 100(1-3), 1630.
doi10.1016/j.jphysparis.2006.09.007 Yang, S.,
Weiner, B. D., Zhang, L. S., Cho, S. J., Bao,
S. (2011). Homeostatic plasticity drives tinnitus
perception in an animal model. Proceedings of the
National Academy of Sciences of the United States
of America, 108(36), 1497414979.
doi10.1073/pnas.1107998108
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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