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State estimation and prediction based on dynamic spike train decoding: noise, adaptation, and multisensory integration
This video was recorded at Workshop on Approximate Inference in Stochastic Processes and Dynamical Systems, Cumberland Lodge 2008. A key requirement facing organisms, or agents in general, acting in uncertain dynamic environments is the real-time estimation and prediction of environmental states, based upon which effective actions can be selected. In this work we show how an agent may use a simple real time neural network, receiving noisy multisensory input signals, to solve these tasks effectively.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
