The goal of our research is to uncover general principles of computation in neural circuits, and more particularly, cortical circuits. Answering this question is critical to understanding how the brain performs a wide variety of tasks, including seeing, hearing, representing the external world, making decisions or controlling motor behavior.
We choose to focus on this general problem rather than any of those specific issues because there is mounting evidence that they all share common computational principles. For instance, the fact that the neural circuitry is remarkably similar throughout the cortex is a strong indication that there is one, or several, general principles at work, independent of the particular function being implemented. Interestingly, the last few years have seen the emergence of a general theory of neural computation which provides a unifying framework to understand human behavior in wide variety of seemingly unrelated domains such as visual perception, cue integration, multisensory integration, decision making, language acquisition, concept acquisition and motor control. This theory is known as the theory of statistical inferences, or Bayesian inferences. The long term goal of my laboratory is to understand how these Bayesian inferences are implemented in neural circuits. Our work is based primarily on a combination of simulations and mathematical analysis of the dynamics of large neural networks with more specific applications to multisensory integration, sensorimotor transformations, decision making, perceptual learning, motor control and attention. We also collaborate with experimentalist to test various aspects of our theory.
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Reference frames for representing the location of visual and tactile stimuli in the parietal cortex.
Nature Neuroscience. 8(6). 2005. PDF
Series, P., Latham, P. and Pouget, A.
Nature Neuroscience. 7(10):1129-1135. 2004. PDF