| Date: |
29 Jan 2004 |
| Title: |
Probabilistic Models of the Brain: Theory and Applications |
| Speaker: |
Prof. Rajesh Rao
University of Washington
Seattle, USA |
| Abstract: |
A large number of neurobiological and psychological results have been
successfully explained in recent years using probabilistic approaches
such as Bayesian models. However, the neural implementation of such
models remains largely unclear. In this talk, we discuss how a network
architecture commonly used to model the cerebral cortex can implement
Bayesian inference for an arbitrary Markov model. We illustrate the
suggested approach using a visual motion detection task. Our simulation
results show that the model network exhibits direction selectivity
similar
to that found in the visual cortex and correctly computes the posterior
probabilities for motion direction. When used to solve a random dots
motion discrimination task, the model generates responses that mimic the
activities of evidence-accumulating neurons in cortical areas LIP and
FEF.
The results provide a new interpretation of neural activities in the
visual cortex as representing posterior probabilities of events
occurring
in the external world. We conclude by exploring the applications of such
models to brain-computer interfaces and robotics.
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