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Abstract
We investigate the general problem of signal classification and, in particular, that of assigning stimulus labels to neural spike trains recorded from single cortical neurons. Finding efficient ways of classifying neural responses is especially important in experiments involving rapid presentation of stimuli. We introduce a fast, exact alternative to Bayesian classification. Instead of estimating the classconditional densities p(xy) (where x is a scalar function of the feature[s], y the class label) and converting them to P(yx) via Bayes' theorem, this probability is evaluated directly and without the need for approximations. This is achieved by integrating over all possible binnings of x with an upper limit on the number of bins. Computational time is quadratic in both the number of observed data points and the number of bins. The algorithm also allows for the computation of feedback signals, which can be used as input to subsequent stages of inference, e.g. neural network training. Responses of single neurons from highlevel visual cortex (area STSa) to rapid sequences of complex visual stimuli are analysed. Information latency and response duration increase nonlinearly with presentation duration, suggesting that neural processing speeds adapt to presentation speeds.
Original language  English 

Pages (fromto)  2135 
Number of pages  15 
Journal  Journal of Computational Neuroscience 
Volume  24 
Issue number  1 
DOIs  
Publication status  Published  Feb 2008 
Keywords
 classification
 exact Bayesian inference
 neural decoding
 MathematicalTheory
 Communication
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 1 Finished

Biologically validated bayesian model of: A biologically validated bayesian model of neural responses to natural stimuli and highlevel visual features
Endres, D. M.
15/03/06 → 14/03/09
Project: Fellowship