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
4NEUROSCIENCE OF LEARNING
HEBB'S THEORY
DONALD O. HEBBS (1904 - 1985) THEORY OF LEARNING
AND MEMORY
NEURONS THAT FIRE TOGETHER WIRE TOGETHER.
4
5HEBB'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
6HEBB'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
7HEBB'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
8HEBB'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
8
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
9
10TRETTENBREINS 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
10
11TRETTENBREINS 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
11
12RESOLUTION OF RECENT CRITIQUES
USING MODERN NEUROPHYSIOLOGICAL RESEARCH
NEUROSCIENCE OF LEARNING
HEBB'S THEORY
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13TRETTENBREINS 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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14RECONCILIATION 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
14
15SYNAPTIC 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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16CELL 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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17THE 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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18USES 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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19NEUROPHYSIOLOGY 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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20CELL 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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21CELL 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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22PHASE 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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23NEUROCOMPUTING
- 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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24HEBBS 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
24
25ABNORMALITIES 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
25
26- 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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27- 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.
27
28NEUROSCIENCE OF LEARNING
HEBB'S THEORY
- REMEMBER -
Practice Makes Perfect!
28
29REFERENCES
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
30REFERENCES
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
31REFERENCES
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
32REFERENCES
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
33REFERENCES
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
34REFERENCES
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
35REFERENCES
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
36REFERENCES
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
37REFERENCES
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
38REFERENCES
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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HEBB'S THEORY
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