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A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
Yuval Harel · Ron Meir · Manfred Opper

Wed Dec 09 12:30 PM -- 01:00 PM (PST) @ Room 210 A
in Spotlight Session 7 »

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.

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Author Information

Yuval Harel (Technion)
Ron Meir (Technion)
Manfred Opper (TU Berlin)

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