A framework for studying synaptic plasticity with neural spike train data

TL;DR

This paper introduces a flexible Bayesian framework for modeling and inferring synaptic plasticity rules from neural spike train data, enabling validation and comparison of different plasticity models.

Contribution

It develops an extensible, fully Bayesian GLM-based approach to infer synaptic weight trajectories and test plasticity rules from large-scale spike train data.

Findings

Successfully recovered synaptic weight trajectories from synthetic data.

Enabled comparison of different STDP rule variants.

Validated the framework's effectiveness on biophysical simulation data.

Abstract

Learning and memory in the brain are implemented by complex, time-varying changes in neural circuitry. The computational rules according to which synaptic weights change over time are the subject of much research, and are not precisely understood. Until recently, limitations in experimental methods have made it challenging to test hypotheses about synaptic plasticity on a large scale. However, as such data become available and these barriers are lifted, it becomes necessary to develop analysis techniques to validate plasticity models. Here, we present a highly extensible framework for modeling arbitrary synaptic plasticity rules on spike train data in populations of interconnected neurons. We treat synaptic weights as a (potentially nonlinear) dynamical system embedded in a fully-Bayesian generalized linear model (GLM). In addition, we provide an algorithm for inferring synaptic weight trajectories alongside the parameters of the GLM and of the learning rules. Using this method, we perform model comparison of two proposed variants of the well-known spike-timing-dependent plasticity (STDP) rule, where nonlinear effects play a substantial role. On synthetic data generated from the biophysical simulator NEURON, we show that we can recover the weight trajectories, the pattern of connectivity, and the underlying learning rules.