Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase of synaptic plasticity. We present a mathematical model that describes these different phases of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency. The stabilization of potentiated synapses during the transition from early to late LTP occurs by protein synthesis dynamics that are shared by groups of synapses. The functional consequence of this shared process is that previously stabilized patterns of strong or weak synapses onto the same postsynaptic neuron are well protected against later changes induced by LTP/LTD protocols at individual synapses.

[1]  T. Bliss,et al.  Long‐lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.

[2]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[3]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  F. Crick.  Neurobiology: Memory and molecular turnover , 1984, Nature.

[5]  F. Crick.  Memory and molecular turnover. , 1984, Nature.

[6]  M. Krug,et al.  Anisomycin blocks the late phase of long-term potentiation in the dentate gyrus of freely moving rats , 1984, Brain Research Bulletin.

[7]  J. Lisman.  A mechanism for memory storage insensitive to molecular turnover: a bistable autophosphorylating kinase. , 1985, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Stanislas Dehaene,et al.  Networks of Formal Neurons and Memory Palimpsests , 1986 .

[9]  S. Grossberg,et al.  ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.

[10]  J. Lisman,et al.  A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[11]  U. Frey,et al.  Dopaminergic antagonists prevent long-term maintenance of posttetanic LTP in the CA1 region of rat hippocampal slices , 1990, Brain Research.

[12]  W. Singer,et al.  Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex , 1990, Nature.

[13]  M. Bear,et al.  Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[14]  W. Singer,et al.  Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation , 1993, Trends in Neurosciences.

[15]  T. Bliss,et al.  A synaptic model of memory: long-term potentiation in the hippocampus , 1993, Nature.

[16]  Daniel J. Amit,et al.  Learning in Neural Networks with Material Synapses , 1994, Neural Computation.

[17]  Wulfram Gerstner,et al.  A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.

[18]  Peter Dayan,et al.  A Neural Substrate of Prediction and Reward , 1997, Science.

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

[20]  J. Lisman,et al.  Postsynaptic Inhibitors of Calcium/Calmodulin-Dependent Protein Kinase Type II Block Induction But Not Maintenance of Pairing-Induced Long-Term Potentiation , 1997, The Journal of Neuroscience.

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

[22]  U. Frey,et al.  Synaptic tagging and long-term potentiation , 1997, Nature.

[23]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[24]  J. Hopfield,et al.  All-or-none potentiation at CA3-CA1 synapses. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[25]  D. Buonomano,et al.  Cortical plasticity: from synapses to maps. , 1998, Annual review of neuroscience.

[26]  H. Markram,et al.  Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[27]  R. Kempter,et al.  Hebbian learning and spiking neurons , 1999 .

[28]  Angelo Arleo,et al.  Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity , 2000, Biological Cybernetics.

[29]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[30]  A. Artola,et al.  Synaptic Activity Modulates the Induction of Bidirectional Synaptic Changes in Adult Mouse Hippocampus , 2000, The Journal of Neuroscience.

[31]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[32]  J. Lisman,et al.  The molecular basis of CaMKII function in synaptic and behavioural memory , 2002, Nature Reviews Neuroscience.

[33]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[34]  Stefano Fusi,et al.  Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates , 2002, Biological Cybernetics.

[35]  W. Abraham.  How long will long-term potentiation last? , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[36]  J. Lisman.  Long-term potentiation: outstanding questions and attempted synthesis. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[37]  M. Bear,et al.  LTP and LTD An Embarrassment of Riches , 2004, Neuron.

[38]  S. Sajikumar,et al.  Late-associativity, synaptic tagging, and the role of dopamine during LTP and LTD , 2004, Neurobiology of Learning and Memory.

[39]  S. Sajikumar,et al.  Resetting of ‘synaptic tags’ is time- and activity-dependent in rat hippocampal CA1in vitro , 2004, Neuroscience.

[40]  Upinder S. Bhalla,et al.  Molecular Switches at the Synapse Emerge from Receptor and Kinase Traffic , 2005, PLoS Comput. Biol..

[41]  S. Wang,et al.  Graded bidirectional synaptic plasticity is composed of switch-like unitary events. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Xiao-Jing Wang,et al.  The Stability of a Stochastic CaMKII Switch: Dependence on the Number of Enzyme Molecules and Protein Turnover , 2005, PLoS biology.

[43]  S. Wang,et al.  Dissection of bidirectional synaptic plasticity into saturable unidirectional processes. , 2005, Journal of neurophysiology.

[44]  L. Abbott,et al.  Cascade Models of Synaptically Stored Memories , 2005, Neuron.

[45]  Wulfram Gerstner,et al.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.

[46]  T. Sacktor,et al.  Synaptic Tagging and Cross-Tagging: The Role of Protein Kinase Mζ in Maintaining Long-Term Potentiation But Not Long-Term Depression , 2005, The Journal of Neuroscience.

[47]  Tobias Bonhoeffer,et al.  Neuronal activity determines the protein synthesis dependence of long-term potentiation , 2006, Nature Neuroscience.

[48]  E. Pastalkova,et al.  Storage of Spatial Information by the Maintenance Mechanism of LTP , 2006, Science.

[49]  S. Tonegawa,et al.  A clustered plasticity model of long-term memory engrams , 2006, Nature Reviews Neuroscience.

[50]  Jean-Pascal Pfister,et al.  Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning , 2005, Neural Computation.

[51]  W. Gerstner,et al.  Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity , 2006, The Journal of Neuroscience.

[52]  D. A. Baxter,et al.  A model of the roles of essential kinases in the induction and expression of late long-term potentiation. , 2006, Biophysical journal.

[53]  J. Frey,et al.  The late maintenance of hippocampal LTP: Requirements, phases, ‘synaptic tagging’, ‘late-associativity’ and implications , 2007, Neuropharmacology.

[54]  Jean-Pascal Pfister,et al.  Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution , 2007, Neural Computation.

[55]  S. Sajikumar,et al.  Synergistic requirements for the induction of dopaminergic D1/D5-receptor-mediated LTP in hippocampal slices of rat CA1 in vitro , 2007, Neuropharmacology.

[56]  E. Izhikevich.  Solving the distal reward problem through linkage of STDP and dopamine signaling , 2007, BMC Neuroscience.

[57]  S. Sajikumar,et al.  Identification of Compartment- and Process-Specific Molecules Required for “Synaptic Tagging” during Long-Term Potentiation and Long-Term Depression in Hippocampal CA1 , 2007, The Journal of Neuroscience.

[58]  Nicolas Brunel,et al.  STDP in a Bistable Synapse Model Based on CaMKII and Associated Signaling Pathways , 2007, PLoS Comput. Biol..

[59]  W. Gerstner,et al.  Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. , 2008, Journal of neurophysiology.

[60]  Robert A. Legenstein,et al.  A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback , 2008, PLoS Comput. Biol..

[61]  M. Ehlers,et al.  Glutamate Receptor Dynamics in Dendritic Microdomains , 2008, Neuron.