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      Phenomenological models of synaptic plasticity based on spike timing

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          Abstract

          Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.

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          Most cited references72

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          Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs.

          Activity-driven modifications in synaptic connections between neurons in the neocortex may occur during development and learning. In dual whole-cell voltage recordings from pyramidal neurons, the coincidence of postsynaptic action potentials (APs) and unitary excitatory postsynaptic potentials (EPSPs) was found to induce changes in EPSPs. Their average amplitudes were differentially up- or down-regulated, depending on the precise timing of postsynaptic APs relative to EPSPs. These observations suggest that APs propagating back into dendrites serve to modify single active synaptic connections, depending on the pattern of electrical activity in the pre- and postsynaptic neurons.
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            Competitive Hebbian learning through spike-timing-dependent synaptic plasticity.

            Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. One form of experimentally observed long-term synaptic plasticity, which we call spike-timing-dependent plasticity (STDP), depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, we find that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced regime. Synapses modifiable by STDP compete for control of the timing of postsynaptic action potentials. Inputs that fire the postsynaptic neuron with short latency or that act in correlated groups are able to compete most successfully and develop strong synapses, while synapses of longer-latency or less-effective inputs are weakened.
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              Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type.

              Q Bi, G Bi, M Poo (1998)
              In cultures of dissociated rat hippocampal neurons, persistent potentiation and depression of glutamatergic synapses were induced by correlated spiking of presynaptic and postsynaptic neurons. The relative timing between the presynaptic and postsynaptic spiking determined the direction and the extent of synaptic changes. Repetitive postsynaptic spiking within a time window of 20 msec after presynaptic activation resulted in long-term potentiation (LTP), whereas postsynaptic spiking within a window of 20 msec before the repetitive presynaptic activation led to long-term depression (LTD). Significant LTP occurred only at synapses with relatively low initial strength, whereas the extent of LTD did not show obvious dependence on the initial synaptic strength. Both LTP and LTD depended on the activation of NMDA receptors and were absent in cases in which the postsynaptic neurons were GABAergic in nature. Blockade of L-type calcium channels with nimodipine abolished the induction of LTD and reduced the extent of LTP. These results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb's rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.
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                Author and article information

                Contributors
                wulfram.gerstner@epfl.ch
                Journal
                Biol Cybern
                Biological Cybernetics
                Springer-Verlag (Berlin/Heidelberg )
                0340-1200
                1432-0770
                20 May 2008
                20 May 2008
                June 2008
                : 98
                : 6
                : 459-478
                Affiliations
                [1 ]Computational Neuroscience Group, RIKEN Brain Science Institute, Wako City, Japan
                [2 ]Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg, Germany
                [3 ]Laboratory of Computational Neuroscience, LCN, Brain Mind Institute and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 15, 1015 Lausanne, Switzerland
                Article
                233
                10.1007/s00422-008-0233-1
                2799003
                18491160
                f36d4f05-4d37-4e44-a31f-458f2a52abc4
                © The Author(s) 2008
                History
                : 16 January 2008
                : 9 April 2008
                Categories
                Review
                Custom metadata
                © Springer-Verlag 2008

                Robotics
                spike-timing dependent plasticity,simulation,learning,short term plasticity,modeling
                Robotics
                spike-timing dependent plasticity, simulation, learning, short term plasticity, modeling

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