Title: Bayesian Receptive Fields and Neural Couplings with Sparsity Prior and Error Bars
Abstract: Here we apply Bayesian system identification methods to infer stimulus-neuron and neuron-neuron dependencies. Rather than reporting only the most likely parameters, the posterior distribution obtained in the Bayesian approach informs us about the range of parameter values that are consistent with the observed data and the assumptions made. In other words, Bayesian receptive fields always come with error bars. In fact, we obtain the full posterior covariance, indicating conditional (in-)dependence between the weights of both, receptive fields and neural couplings. Since the amount of data from neural recordings is limited, such uncertainty information is as important as the usual point estimate of the receptive field itself.
Publication Year: 2007
Publication Date: 2007-02-01
Language: en
Type: article
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