Stochastic Models of Neural Synaptic Plasticity

TL;DR

This paper introduces a new mathematical framework for modeling synaptic plasticity in neural networks, focusing on stochastic processes and STDP rules, with analysis of Markovian subclasses and their biological implications.

Contribution

A novel, general stochastic modeling framework for synaptic plasticity that unifies various STDP rules and incorporates Markovian properties of cellular processes.

Findings

The framework can represent many existing STDP models.

Analysis of Markovian subclasses reveals insights into neural dynamics.

Comparison with canonical models highlights the framework's versatility.

Abstract

In neuroscience, learning and memory are usually associated to long-term changes of neuronal connectivity. In this context, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called {\em synapses} and represented by a scalar value, the synaptic weight. Spike-Timing Dependent Plasticity (STDP) is a biologically-based model representing the time evolution of the synaptic weight as a functional of the past spiking activity of adjacent neurons. If numerous models of neuronal cells have been proposed in the mathematical literature, few of them include a variable for the time-varying strength of the connection. A new, general, mathematical framework is introduced to study synaptic plasticity associated to different STDP rules. The system composed of two neurons connected by a single synapse is investigated and a stochastic process describing its dynamical behavior is presented and analyzed. The notion of plasticity kernel is introduced as a key component of plastic neural networks models, generalizing a notion used for pair-based models. We show that a large number of STDP rules from neuroscience and physics can be represented by this formalism. Several aspects of these models are discussed and compared to canonical models of computational neuroscience. An important sub-class of plasticity kernels with a Markovian formulation is also defined and investigated. In these models, the time evolution of cellular processes such as the neuronal membrane potential and the concentrations of chemical components created/suppressed by spiking activity has the Markov property.