An Analytically Tractable Bayesian Approximation to Optimal Point Process Filtering

TL;DR

This paper introduces an analytically tractable Bayesian approximation for optimal point process filtering, providing new insights into sensory encoding strategies and highlighting the importance of non-spike information in neural decoding.

Contribution

It develops a novel analytical framework for Bayesian filtering with point processes, enabling analysis beyond uniform coding assumptions in computational neuroscience.

Findings

The framework aligns with experimental data on tuning curve distributions.

Absence of spikes carries significant information for filtering performance.

Analytical approach offers insights difficult to obtain from numerical methods.

Abstract

The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscience. We develop an analytically tractable Bayesian approximation to optimal filtering based on point process observations, which allows us to introduce distributional assumptions about sensory cell properties, that greatly facilitates the analysis of optimal encoding in situations deviating from common assumptions of uniform coding. The analytic framework leads to insights which are difficult to obtain from numerical algorithms, and is consistent with experiments about the distribution of tuning curve centers. Interestingly, we find that the information gained from the absence of spikes may be crucial to performance.