2000/06/30 by Simon R. Schultz, Huw Golledge, Huw D. R. Golledge +4
Biochemistry, Genetics and Molecular Biology · Neuroscience · Physics and Astronomy · #Biological Physics (physics.bio-ph) #FOS: Biological sciences #FOS: Physical sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Photoreceptor and optogenetics research #physics.bio-ph #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.physics/0006081
15 pages, to appear in Emergent Neural Computational Architectures Based on Neuroscience, Lecture Notes in Computer Science, Springer-Verlag, Heidelberg
arxiv created 2000/06/30 · openalex publication_date 2000/06/30 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Does synchronization between action potentials from different neurons in the visual system play a substantial role in solving the binding problem? The binding problem can be studied quantitatively in the broader framework of the information contained in neural spike trains about some external correlate, which in this case is object configurations in the visual field. We approach this problem by using a mathematical formalism that quantifies the impact of correlated firing in short time scales. Using a power series expansion, the mutual information an ensemble of neurons conveys about external stimuli is broken down into firing rate and correlation components. This leads to a new quantification procedure directly applicable to simultaneous multiple neuron recordings. It theoretically constrains the neural code, showing that correlations contribute less significantly than firing rates to rapid information processing. By using this approach to study the limits upon the amount of information that an ideal observer is able to extract from a synchrony code, it may be possible to determine whether the available amount of information is sufficient to support computational processes such as feature binding.