A model of spike-timing dependent plasticity: one or two coincidence detectors?

In spike-timing dependent plasticity (STDP), synapses exhibit LTD or LTP depending on the order of activity in the presynaptic and postsynaptic cells. LTP occurs when a single presynaptic spike precedes a postsynaptic one (a positive interspike interval, or ISI), while the reverse order of activity (a negative ISI) produces LTD. A fundamental question is whether the "standard model" of plasticity in which moderate increases in Ca(2+) influx through the N-methyl-D-aspartate (NMDA) channels induce LTD and large increases induce LTP, can account for the order and interval sensitivity of STDP. To examine this issue we developed a model that captures postsynaptic Ca(2+) influx dynamics and the associativity of the NMDA receptors. While this model can generate both LTD and LTP, it predicts that LTD will be observed at both negative and positive ISIs. This is because longer and longer positive ISIs induce monotonically decreasing levels of Ca(2+), which eventually fall into the same range that produced LTD at negative ISIs. A second model that incorporated a second coincidence detector in addition to the NMDA receptor generated LTP at positive intervals and LTD only at negative ones. Our findings suggest that a single coincidence detector model based on the standard model of plasticity cannot account for order-specific STDP, and we predict that STDP requires two coincidence detectors.

[1]  W. Levy,et al.  Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus , 1983, Neuroscience.

[2]  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.

[3]  CE Jahr,et al.  NMDA channel behavior depends on agonist affinity , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  C. Stevens,et al.  NMDA receptors at excitatory synapses in the hippocampus: test of a theory of magnesium block , 1993, Neuroscience Letters.

[5]  D. Debanne,et al.  Asynchronous pre- and postsynaptic activity induces associative long-term depression in area CA1 of the rat hippocampus in vitro. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[6]  R. Nicoll,et al.  Ca2+ Signaling Requirements for Long-Term Depression in the Hippocampus , 1996, Neuron.

[7]  M. Bear,et al.  Long-term depression in hippocampus. , 1996, Annual review of neuroscience.

[8]  K. I. Blum,et al.  Functional significance of long-term potentiation for sequence learning and prediction. , 1996, Cerebral cortex.

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

[10]  R. Nicoll,et al.  Two Distinct Forms of Long-Term Depression Coexist in CA1 Hippocampal Pyramidal Cells , 1997, Neuron.

[11]  F. Crépel,et al.  Cellular mechanisms of cerebellar LTD , 1998, Trends in Neurosciences.

[12]  Li I. Zhang,et al.  A critical window for cooperation and competition among developing retinotectal synapses , 1998, Nature.

[13]  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.

[14]  D. Debanne,et al.  Long‐term synaptic plasticity between pairs of individual CA3 pyramidal cells in rat hippocampal slice cultures , 1998, The Journal of physiology.

[15]  M. Bear,et al.  Role for rapid dendritic protein synthesis in hippocampal mGluR-dependent long-term depression. , 2000, Science.

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

[17]  P. Jonas,et al.  Associative Long-Term Depression in the Hippocampus Is Dependent on Postsynaptic N-Type Ca2+ Channels , 2000, The Journal of Neuroscience.

[18]  M. Poo,et al.  Calcium stores regulate the polarity and input specificity of synaptic modification , 2000, Nature.

[19]  D V Buonomano,et al.  Decoding Temporal Information: A Model Based on Short-Term Synaptic Plasticity , 2000, The Journal of Neuroscience.

[20]  D. Feldman,et al.  Timing-Based LTP and LTD at Vertical Inputs to Layer II/III Pyramidal Cells in Rat Barrel Cortex , 2000, Neuron.

[21]  Henry Markram,et al.  An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing , 2001, Neural Computation.