Probabilistic Population Codes for Bayesian Decision Making

  title={Probabilistic Population Codes for Bayesian Decision Making},
  author={Jeffrey M. Beck and Wei Ji Ma and Roozbeh Kiani and Tim Hanks and Anne K. Churchland and Jamie D Roitman and Michael N. Shadlen and Peter E. Latham and Alexandre Pouget},
When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through… CONTINUE READING
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The Wave Theory of Difference and Similarity (Hillsdale, NJ: Lawrence Erlbaum Associates)

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