Research

Computational Psychiatry & Decision-making

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Abstracts

  • pdf Probabilistic computation in spiking populations
  • Zemel R, Huys QJM, Natarajan R and Dayan P
  • NIPS 2004
  • As animals interact with their environments, they must constantly update estimates about their states. Bayesian models combine prior probabilities, a dynamical model and sensory evidence to update estimates optimally. These models are consistent with the results of many diverse psychophysical studies. However, little is known about the neural representation and manipulation of such Bayesian information, particularly in populations of spiking neurons. We consider this issue, suggesting a model based on standard neural architecture and activations. We illustrate the approach on a simple random walk example, and apply it to a sensorimotor integration task that provides a particularly compelling example of dynamic probabilistic computation.