Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules

By Harel Z. Shouval, Sharon Samuel, Wang, G. M. Wittenberg. 2010

224 citations recorded by the source corpus.

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse – the “first law” of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Computational models of long term plasticity and memory (2017, background): “Although the computational principles behind STDP are probably general and important, experiments on synaptic plasticity show that STDP is only one of the aspects of the mechanisms for the induction of long term changes (see e.g. [Shouval et al., 2010]).”
Spike-Timing-Dependent Plasticity: A Comprehensive Overview (2012, background): “In a more specific argument, Shouval et al. (2010) suggest that STDP is in reality a result of something more fundamental, which they propose is intracellular calcium signals.”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, background): “It is important to remember however that STDP is not a fundamental description of synaptic plasticity, but simply an experimental observation that describes how synapses respond to one particular stimulus of temporally offset spike pairings [3–6].”
The spike timing dependence of plasticity (2012, background): “These derive from concerns that postsynaptic spikes and spike timing are relatively minor factors for plasticity under natural network conditions, and therefore that STDP is not a particularly accurate or useful description of natural plasticity (Lisman and Spruston, 2005, 2010; Shouval et al., 2010”
An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation (2011, unknown): “There are different forms of STDP-like behaviors that can enable adaptive learning as illustrated in [ 41 ].”