Interplay between Short- and Long-Term Plasticity in Cell-Assembly Formation

Various hippocampal and neocortical synapses of mammalian brain show both short-term plasticity and long-term plasticity, which are considered to underlie learning and memory by the brain. According to Hebb’s postulate, synaptic plasticity encodes memory traces of past experiences into cell assemblies in cortical circuits. However, it remains unclear how the various forms of long-term and short-term synaptic plasticity cooperatively create and reorganize such cell assemblies. Here, we investigate the mechanism in which the three forms of synaptic plasticity known in cortical circuits, i.e., spike-timing-dependent plasticity (STDP), short-term depression (STD) and homeostatic plasticity, cooperatively generate, retain and reorganize cell assemblies in a recurrent neuronal network model. We show that multiple cell assemblies generated by external stimuli can survive noisy spontaneous network activity for an adequate range of the strength of STD. Furthermore, our model predicts that a symmetric temporal window of STDP, such as observed in dopaminergic modulations on hippocampal neurons, is crucial for the retention and integration of multiple cell assemblies. These results may have implications for the understanding of cortical memory processes.

[1]  Mark C. W. van Rossum,et al.  Memory retention and spike-timing-dependent plasticity. , 2009, Journal of neurophysiology.

[2]  Richard Hans Robert Hahnloser,et al.  Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity , 2010, Neuron.

[3]  D. Johnston,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997 .

[4]  Paolo Del Giudice,et al.  Long and short-term synaptic plasticity and the formation of working memory: A case study , 2001, Neurocomputing.

[5]  G. Bi,et al.  Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses , 2009, Proceedings of the National Academy of Sciences.

[6]  Markus Diesmann,et al.  Spike-Timing-Dependent Plasticity in Balanced Random Networks , 2007, Neural Computation.

[7]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[8]  Daniel J. Amit,et al.  Mean-field analysis of selective persistent activity in presence of short-term synaptic depression , 2006, Journal of Computational Neuroscience.

[9]  Hilbert J. Kappen,et al.  Associative Memory with Dynamic Synapses , 2002, Neural Computation.

[10]  Wulfram Gerstner,et al.  Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector , 2013, PLoS Comput. Biol..

[11]  Claire E. J. Cheetham,et al.  Presynaptic Development at L4 to L2/3 Excitatory Synapses Follows Different Time Courses in Visual and Somatosensory Cortex , 2010, The Journal of Neuroscience.

[12]  Tomoki Fukai,et al.  Associative memory model with long-tail-distributed Hebbian synaptic connections , 2013, Front. Comput. Neurosci..

[13]  Andrew Philippides,et al.  Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus , 2010, PLoS Comput. Biol..

[14]  Matthieu Gilson,et al.  Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence , 2010, Biological Cybernetics.

[15]  James L. McClelland,et al.  Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. , 1995, Psychological review.

[16]  D. Debanne,et al.  Paired pulse facilitation and depression at unitary excitatory synapses in the rat hippocampus in vitro , 1994, Journal of Physiology-Paris.

[17]  G. Buzsáki,et al.  The log-dynamic brain: how skewed distributions affect network operations , 2014, Nature Reviews Neuroscience.

[18]  J. Born,et al.  The memory function of sleep , 2010, Nature Reviews Neuroscience.

[19]  Wolfgang Maass,et al.  Emergence of Dynamic Memory Traces in Cortical Microcircuit Models through STDP , 2013, The Journal of Neuroscience.

[20]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[21]  D Debanne,et al.  Paired‐pulse facilitation and depression at unitary synapses in rat hippocampus: quantal fluctuation affects subsequent release. , 1996, The Journal of physiology.

[22]  D. Alkon,et al.  Enhancement of long-term memory retention and short-term synaptic plasticity in cbl-b null mice. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[23]  P. Dayan,et al.  Matching storage and recall: hippocampal spike timing–dependent plasticity and phase response curves , 2005, Nature Neuroscience.

[24]  S. Nelson,et al.  Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.

[25]  W. Gerstner,et al.  Connectivity reflects coding: a model of voltage-based STDP with homeostasis , 2010, Nature Neuroscience.

[26]  W. K. Cullen,et al.  Naturally secreted oligomers of amyloid β protein potently inhibit hippocampal long-term potentiation in vivo , 2002, Nature.

[27]  G. Edelman,et al.  Spike-timing dynamics of neuronal groups. , 2004, Cerebral cortex.

[28]  Marian Joëls,et al.  Mineralocorticoid receptors are indispensable for nongenomic modulation of hippocampal glutamate transmission by corticosterone. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  F. Kimura,et al.  Developmental Switch in Spike Timing-Dependent Plasticity at Layers 4–2/3 in the Rodent Barrel Cortex , 2012, The Journal of Neuroscience.

[30]  G. Laurent,et al.  Corrigendum: Conditional modulation of spike-timing-dependent plasticity for olfactory learning , 2012, Nature.

[31]  Isaac Meilijson,et al.  Distributed synchrony in a cell assembly of spiking neurons , 2001, Neural Networks.

[32]  Tomoki Fukai,et al.  Noise-tolerant stimulus discrimination by synchronization with depressing synapses , 2001, Biological Cybernetics.

[33]  Haim Sompolinsky,et al.  Chaotic Balanced State in a Model of Cortical Circuits , 1998, Neural Computation.

[34]  T. Hensch Critical period plasticity in local cortical circuits , 2005, Nature Reviews Neuroscience.

[35]  Matthieu Gilson,et al.  Frontiers in Computational Neuroscience Computational Neuroscience , 2022 .

[36]  N. Brunel,et al.  Irregular persistent activity induced by synaptic excitatory feedback , 2007, BMC Neuroscience.

[37]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[38]  C. Sandi Glucocorticoids act on glutamatergic pathways to affect memory processes , 2011, Trends in Neurosciences.

[39]  Matthieu Gilson,et al.  Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV , 2009, Biological Cybernetics.

[40]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[41]  Mark C. W. van Rossum,et al.  Recurrent networks with short term synaptic depression , 2009, Journal of Computational Neuroscience.

[42]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

[43]  Joaquín J. Torres,et al.  Chaotic hopping between attractors in neural networks , 2006, Neural Networks.

[44]  P. Lewis,et al.  Overlapping memory replay during sleep builds cognitive schemata , 2011, Trends in Cognitive Sciences.

[45]  J. O’Neill,et al.  Play it again: reactivation of waking experience and memory , 2010, Trends in Neurosciences.

[46]  J. Born,et al.  Sleep enhances false memories depending on general memory performance , 2010, Behavioural Brain Research.

[47]  R. Morris,et al.  Making memories last: the synaptic tagging and capture hypothesis , 2010, Nature Reviews Neuroscience.

[48]  I. Slutsky,et al.  Amyloid-β as a positive endogenous regulator of release probability at hippocampal synapses , 2009, Nature Neuroscience.

[49]  Matthieu Gilson,et al.  Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma , 2011, PloS one.

[50]  Christos Dimitrakakis,et al.  Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex , 2013, PLoS Comput. Biol..

[51]  György Buzsáki,et al.  Neural Syntax: Cell Assemblies, Synapsembles, and Readers , 2010, Neuron.

[52]  Y. Frégnac,et al.  Stable Learning in Stochastic Network States , 2012, The Journal of Neuroscience.

[53]  Tomoki Fukai,et al.  Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links , 2012, Scientific Reports.

[54]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

[55]  David Hansel,et al.  Short-Term Plasticity Explains Irregular Persistent Activity in Working Memory Tasks , 2013, The Journal of Neuroscience.

[56]  Anne-Marie M Oswald,et al.  Maturation of intrinsic and synaptic properties of layer 2/3 pyramidal neurons in mouse auditory cortex. , 2008, Journal of neurophysiology.

[57]  P. J. Sjöström,et al.  Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity , 2001, Neuron.

[58]  A. Litwin-Kumar,et al.  Slow dynamics and high variability in balanced cortical networks with clustered connections , 2012, Nature Neuroscience.

[59]  Jessica D. Payne,et al.  Human relational memory requires time and sleep , 2007, Proceedings of the National Academy of Sciences.

[60]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.