Fitting summary statistics of neural data with a differentiable spiking network simulator

TL;DR

This paper introduces a differentiable spiking network simulator that improves the realism of neural activity models by incorporating dissimilarity measures based on summary statistics, enabling more accurate fitting and inference of neural networks.

Contribution

It proposes a novel fitting method that combines likelihood maximization with dissimilarity measures and back-propagation, enhancing the realism and inference capabilities of neural network models.

Findings

Produces more realistic neural activity statistics

Outperforms traditional fitting algorithms like GLMs

Enables inference of hidden neurons and network connectivity

Abstract

Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is widely used, we show that the resulting model does not produce realistic neural activity. To correct for this, we suggest to augment the log-likelihood with terms that measure the dissimilarity between simulated and recorded activity. This dissimilarity is defined via summary statistics commonly used in neuroscience and the optimization is efficient because it relies on back-propagation through the stochastically simulated spike trains. We analyze this method theoretically and show empirically that it generates more realistic activity statistics. We find that it improves upon other fitting algorithms for spiking network models like GLMs (Generalized Linear Models) which do not usually rely on back-propagation. This new fitting algorithm also enables the consideration of hidden neurons which is otherwise notoriously hard, and we show that it can be crucial when trying to infer the network connectivity from spike recordings.