An Analytically Tractable Bayesian Approximation to Optimal Point Process Filtering
This work addresses a fundamental challenge in computational neuroscience by providing conceptual insights into optimal encoding and decoding strategies, though it is incremental as it builds on existing filtering methods with new approximations.
The authors tackled the intractable problem of dynamic state estimation from point process observations by developing an analytically tractable Bayesian approximation, which revealed that information from the absence of spikes can be crucial to performance and is consistent with experimental data on tuning curve distributions.
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.