Two-trace model for spike-timing-dependent synaptic plasticity

TL;DR

This paper introduces a two-trace model for spike-timing-dependent plasticity that captures key biological mechanisms and bridges simple phenomenological rules with detailed models, useful for neural network studies.

Contribution

The model simplifies STDP representation with only three parameters, reproduces triplet nonlinearities, and explores the transition from timing-dependent to rate-dependent plasticity.

Findings

Reproduces standard pairwise STDP rules

Adjustable parameters fit triplet nonlinearities in hippocampal and cortical data

Analyzes transition from timing-dependent to rate-dependent plasticity

Abstract

We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the Ca2+ concentration in the dendritic spine of the postsynaptic neuron. This model intends to bridge the worlds of existing simplistic phenomenological rules and highly detailed models, constituting thus a practical tool for the study of the interplay between neural activity and synaptic plasticity in extended spiking neural networks. For isolated pairs of pre- and postsynaptic spikes the standard pairwise STDP rule is reproduced, with appropriate parameters determining the respective weights and time scales for the causal and the anti-causal contributions. The model contains otherwise only three free parameters which can be adjusted to reproduce triplet nonlinearities in both hippocampal culture and cortical slices. We also investigate the transition from time-dependent to rate-dependent plasticity occurring for both correlated and uncorrelated spike patterns.