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LEE LAB

for Biological and Machine Intelligence Research

Decoding Visual Input based on V1 Neuronal Activities with Particle Filtering

Ryan C.Kelly, Tai Sing Lee | Neurocomputing | 2004 | 10.1016/j.neucom.2004.01.137 | PDF
In this study, we investigated the use of particle filtering in reconstructing timevarying input visual signals based on Macaque V1 neurons’ responses. A multitude of hypothesis particles are proposed for reconstructing the input stimulus up to time t. A prediction kernel (consisting of the first and second order forward Wiener kernels, derived by regression) is used to predict the neural response at time t based on the estimated input signals in the 200 ms prior to t. The fitness of this estimated response in predicting the measured response at time t is used to weigh the importance of the various hypotheses. The hypothesis particle space is collapsed by re-sampling over time. We find this method quite successful in reconstructing the input stimulus for 30 out of 33 V1 neurons measured. It out-performs the optimal linear decoder that we have experimented with in the past (Romero and Lee, 2002).