2015/07/28 by Yuval Harel, Harel, Yuval, Ron Meir +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · #Algorithm #Artificial intelligence #Bayesian probability #Coding (social sciences) #Computer science #Decoding methods #Encoding (memory) #FOS: Biological sciences #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Mathematical optimization #Mathematics #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Point (geometry) #Point process #Prior probability #Process (computing) #Relevance (law) #Target Tracking and Data Fusion in Sensor Networks #q-bio.NC #stat.ML
paper · pdf · doi:10.48550/arxiv.1507.07813
arxiv created 2015/07/28 · openalex publication_date 2015/07/28 · arxiv updated 2015/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.