Paper Info
Title
Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels
Abstract
Decoding is a strategy that allows us to assess the amount of information neurons can provide about certain aspects of the visual scene. In this study, we develop a method based on Bayesian sequential updating and the particle filtering algorithm to decode the activity of V1 neurons in awake monkeys. A distinction in our method is the use of Volterra kernels to filter the particles, which live in a high dimensional space. This parametric Bayesian decoding scheme is compared to the optimal linear decoder and is shown to work consistently better than the linear optimal decoder. Interestingly, our results suggest that for decoding in real time, spike trains of as few as 10 independent but similar neurons would be sufficient for decoding a critical scene variable in a particular class of visual stimuli. The reconstructed variable can predict the neural activity about as well as the actual signal with respect to the Volterra kernels.
Year
Venue
Keywords
2003
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16
neuronal activity,particle filter,bayesian method,real time,belief propagation,linear optimization
Field
DocType
Volume
Premovement neuronal activity,Computer science,Particle filter,Neural activity,Parametric statistics,Artificial intelligence,High dimensional space,Decoding methods,Visual perception,Machine learning,Bayesian probability
Conference
16
ISSN
Citations 
PageRank 
1049-5258
4
0.57
References 
Authors
4
2
Name
Order
Citations
PageRank
Kelly, Ryan1413.60
Tai Sing Lee279488.73