N C E O F L E A R N I N G H E B B ’ S T H E O R Y R A C H E L H O N G H E B B ' S T H E O R Y N E U R O S C I E N C E O F L E A R N I N G P S Y C H O L O G Y O F L E A R N I N G
I O N C O N T E N T S Donald O. Hebb’s Theory of Learning and Memory Trettenbrein’s 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 Hebb’s 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 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
S L I T E R A T U R E O N T H E N E U R O B I O L O G Y O F L E A R N I N G A N D M E M O R Y 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 Hebb’s (1949) neuropsychological theory. N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
E B B ’ S ( 1 9 0 4 - 1 9 8 5 ) T H E O R Y O F L E A R N I N G A N D M E M O R Y N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y N E U R O N S T H A T F I R E T O G E T H E R W I R E T O G E T H E R .
O R Y Hebb’s (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. Hebb’s 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 A’s efficiency, as one of the cells firing B, is increased.” N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
O R Y 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 Hebb’s 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y 1, 4 5, 9 7, 15 13 12 3, 11 2, 14 6, 10 8 C E L L A S S E M B L Y
O R Y 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y P H A S E S E Q U E N C E
O R Y 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). Hebb’s 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. Hebb’s assumption contains two concepts: synaptic plasticity and “some growth process or metabolic change” in the neuron, which is “intrinsic plasticity” (Titley, Brunel, & Hansel, 2017). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
T E R 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 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
R E I N ’ S C R I T I Q U E S O F T H E N E U R O P H Y S I O L O G I C E X P L A N A T I O N O F L E A R N I N G A N D M E M O R Y N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y 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”.
R E I N ’ S C R I T I Q U E S 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 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
O N O F R E C E N T C R I T I Q U E S N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y U S I N G M O D E R N N E U R O P H Y S I O L O G I C A L R E S E A R C H
R E I N ’ S C R I T I Q U E S The critique of synaptic plasticity theory proposed by Trettenbrein (2016) can be resolved using Hebb’s 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. N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
I A T I O N O F T H E D I F F E R E N C E S B E T W E E N P H Y S I O L O G I S T S A N D P S Y C H O L O G I S T S A B O U T T H E R O L E O F S Y N A P T I C P L A S T I C I T Y I N L E A R N I N G A N D M E M O R Y N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y 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).
C H A N G E A N D T H E F O R M A T I O N O F C E L L A S S E M B L I E S A R E F U N D A M E N T A L F O R T H E O R I E S O F M E M O R Y 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. N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
M B L I E S H A V E B E E N V E R I F I E D B Y N E U R O I M A G I N G Hebb’s theories on the neurophysiological basis of learning and memory integrate synaptic neurophysiology with psychological concepts like attention, perception, thought and mind—the concepts which Pavlov avoided in his objective approach to memory. Hebb’s theory effectively integrated Pavlov’s concepts of the physiology of learning with Lashley’s (1932) criticism that Pavlov ignored psychological concepts. Neuroimaging studies have shown the usefulness of Hebb’s 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. O’Neil et al. (2012) found that different cortical regions were activated in recognition memory. N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
T A N C E O F H E B B ’ S I D E A S There is still existence on the Hebb synapse (Brown, 2020). To name a few: Graham Collingridge’s paper on Hebb synapses and beyond Ole Paulson’s paper on Neuromodulation of Hebbian synapses Zahid Padamsey’s 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. N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
B B ’ S W O R K T O D A Y Learning and memory Long-term effects of the environment on development Aging Neurocomputing Artificial intelligence Robotics N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
S I O L O G Y O F L E A R N I N G A N D M E M O R Y The proposal that long-term potentiation was a synaptic model of memory (Bliss, & Collingridge, 1993) led to a number of examinations of Hebb’s 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 Hebb’s 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.” N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
M B L Y 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”. N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
M B L Y 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
U E N C E S Hebb’s 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
P U T I N G 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
N A P T I C L E A R N I N G R U L E A N D C E L L A S S E M B L Y T H E O R Y A R E U S E D I N C O M P U T A T I O N A L N E U R O S C I E N C E A N D R O B O T I C S 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
I T I E S I N S Y N A P T I C P L A S T I C I T Y U N D E R L I E C O G N I T I V E A N D M O T O R D Y S F U N C T I O N S P A I N M E C H A N I S M S A N D D R U G A D D I C T I O N 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
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 A’s 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). N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
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, Parkinson’s disease, Huntington’s disease and multiple sclerosis (Torres, Vallejo, & Inestrosa, 2017) N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y 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 Alzheimer’s disease (Beatty, Salmon, Butters, Heindel, & Granholm, 1988). A synaptic plasticity theory of memory can demonstrate the memory impairments in neuropathologic conditions like Alzheimer’s disease.
E S 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. doi:10.3389/fncir.2014.00034 Andersen, N., Krauth, N., & Nabavi, S. (2017). Hebbian plasticity in vivo: relevance and induction. Current opinion in neurobiology, 45, 188–192. doi:10.1016/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), 6666–6674. doi:10.1073/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), 181–186. doi:10.1016/s0197-4580(88)80048-4 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 Bliss, T. V., & Collingridge, G. L. (1993). A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 361(6407), 31–39. doi:10.1038/361031a0 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y
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), 8–14. doi:10.1177/1073858413491148 Brzosko, Z., Mierau, S. B., & Paulsen, O. (2019). Neuromodulation of Spike-Timing-Dependent Plasticity: Past, Present, and Future. Neuron, 103(4), 563–581. doi:10.1016/j.neuron.2019.05.041 Buzsáki G. (2010). Neural syntax: cell assemblies, synapsembles, and readers. Neuron, 68(3), 362–385. doi:10.1016/j.neuron.2010.09.023 Calderon, D., Baidyk, T., & Kussul, E. (2013). Hebbian ensemble neural network for robot movement control. Optical Memory and Neural Networks, 22(3), 166–183. doi:10.3103/s1060992x13030028 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), 111–124. doi:10.1016/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, 88–93. doi:10.1016/j.neulet.2017.04.006 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
Cognitive Systems Research. doi:10.1016/j.cogsys.2019.09.023 Fuster J. M. (1997). Network memory. Trends in neurosciences, 20(10), 451–459. doi:10.1016/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, 136–144. doi:10.1016/j.nlm.2013.11.019 Harris K. D. (2005). Neural signatures of cell assembly organization. Nature reviews. Neuroscience, 6(5), 399–407. doi:10.1038/nrn1669 Harris K. D. (2012). Cell assemblies of the superficial cortex. Neuron, 76(2), 263–265. doi:10.1016/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; ), 622–643. 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. doi:10.3389/fnbot.2018.00079 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition. Frontiers in systems neuroscience, 12, 52. doi.org/10.3389/fnsys.2018.00052 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), E5584–E5592. doi:10.1073/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), E3679–E3688. doi:10.1073/pnas.1617259114 Kuriscak, E., Marsalek, P., Stroffek, J., & Toth, P. G. (2015). Biological context of Hebb learning in artificial neural networks, a review. Neurocomputing, 152, 27–35. doi:10.1016/j.neucom.2014.11.022 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
behavior. The Journal of neuroscience : the official journal of the Society for Neuroscience, 33(45), 17641–17646. doi10.1523/JNEUROSCI.3406-13.2013 Lansner A. (2009). Associative memory models: from the cell-assembly theory to biophysically detailed cortex simulations. Trends in neurosciences, 32(3), 178–186. doi:10.1016/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), 77–84. doi:10.1002/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. doi:10.3389/fncir.2016.00034 Lisman, 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), 309–314. doi:10.1038/s41593- 018-0076-6 Luo, C., Kuner, T., & Kuner, R. (2014). Synaptic plasticity in pathological pain. Trends in neurosciences, 37(6), 343–355. doi:10.1016/j.tins.2014.04.002 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
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. doi:10.3389/fncom.2017.00119 Markram, H., Gerstner, W., & Sjöström, P. J. (2011). A history of spike-timing-dependent plasticity. Frontiers in synaptic neuroscience, 3, 4. doi:10.3389/fnsyn.2011.00004 McNaughton, B. L. (2003). Long-term potentiation, cooperativity and Hebb’s cell assemblies: a personal history. Philosophical Transactions of the Royal Society B: Biological Sciences, 358(1432), 629–634. doi:10.1098/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, 53–61. doi:10.1016/j.bbr.2014.08.046 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), 328–342. doi:10.1016/j.pnpbp.2017.09.014 Munakata, Y., & Pfaffly, J. (2004). Hebbian learning and development. Developmental science, 7(2), 141–148. doi:10.1111/j.1467-7687.2004.00331.x N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
Hebb's Neural Networks Support the Integration of Psychological Science. Canadian Psychology/Psychologie canadienne, 45(4), 265–278. doi:10.1037/h0086997 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), 74–85. doi:10.1093/cercor/bhr075 Oberauer, K., Jones, T., & Lewandowsky, S. (2015). The Hebb repetition effect in simple and complex memory span. Memory & cognition, 43(6), 852–865. doi:10.3758/s13421-015-0512-8 Palm, G., Knoblauch, A., Hauser, F., & Schüz, A. (2014). Cell assemblies in the cerebral cortex. Biological cybernetics, 108(5), 559–572. doi:10.1007/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. doi:10.1186/s12915-016-0261-6 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
F. (2015). The Use of Hebbian Cell Assemblies for Nonlinear Computation. Scientific reports, 5, 12866. doi:10.1038/srep12866 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, 1–23. doi:10.1146/annurev.psych.58.110405.085516 Pulvermüller, F., Garagnani, M., & Wennekers, T. (2014). Thinking in circuits: toward neurobiological explanation in cognitive neuroscience. Biological cybernetics, 108(5), 573–593. doi:10.1007/s00422-014-0603-9 Sweatt J. D. (2016). Neural plasticity and behavior - sixty years of conceptual advances. Journal of neurochemistry, 139 Suppl 2, 179–199. doi:10.1111/jnc.13580 Takamiya, S., Yuki, S., Hirokawa, J., Manabe, H., & Sakurai, Y. (2019). Dynamics of memory engrams. Neuroscience Research. doi:10.1016/j.neures.2019.03.005 Takeuchi, 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. doi:10.3389/fnhum.2015.00349 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
chromatin remodeling: a missing link in epigenetic mechanisms underlying synaptic plasticity, memory, and intellectual disability disorders. Neuropharmacology, 80, 18– 27. doi:10.1016/j.neuropharm.2013.10.002 Titley, H. K., Brunel, N., & Hansel, C. (2017). Toward a Neurocentric View of Learning. Neuron, 95(1), 19–32. doi:10.1016/j.neuron.2017.05.021 Tomasello, 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. doi:10.3389/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. doi:10.1155/2017/8081758 Trettenbrein P.C. (2016). The demise of the synapse as the locus of memory: a looming paradigm shift?. Frontiers in systems neuroscience 10, 88. doi:10.3389/fnsys.2016.00088 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E 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), 14974–14979. doi:10.1073/pnas.1107998108 Wallace, D. J., & Kerr, J. N. (2010). Chasing the cell assembly. Current opinion in neurobiology, 20(3), 296– 305. Wang X. J. (2012). Neural dynamics and circuit mechanisms of decision-making. Current opinion in neurobiology, 22(6), 1039–1046. doi:10.1016/j.conb.2012.08.006 Weintraub, S., Wicklund, A. H., & Salmon, D. P. (2012). The neuropsychological profile of Alzheimer disease. Cold Spring Harbor perspectives in medicine, 2(4), a006171. doi:10.1101/cshperspect.a006171 Wennekers, T., Garagnani, M., & Pulvermüller, F. (2006). Language models based on Hebbian cell assemblies. Journal of physiology, Paris, 100(1-3), 16–30. doi:10.1016/j.jphysparis.2006.09.007 N E U R O S C I E N C E O F L E A R N I N G H E B B ' S T H E O R Y R E F E R E N C E S
rachelhch
nnchiba
upura
daimoriwaki
masatoto
trafficbrain
hf149
kampersanda
sgk
sshimizu2006
snowberryfield
hiromu1996
notwaldorf
philnash
addyosmani
kevinliebholz
yeseniaperezcruz
bluesmoon
bkeepers
62gerente
samlambert
honnibal
reverentgeek
caitiem20
