Uncovering the Neural Code of Learning Control - Jennie Si - WCCI 2012 invited lectureUncovering the Neural Code of Learning Control - Jennie Si - WCCI 2012 invited lecture

Uncovering the Neural Code of Learning Control - Jennie Si - WCCI 2012 invited lecture

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  • #Neural Code
  • #WCCI 2012
  • #Jennie Si
  • #neuroscience

Jennie Si, Department of Electrical Engineering, Arizona State University, email: si@asu.edu Abstract: How interacting neurons give rise to meaningful behavior is an ultimate challenge to neuroscientists. To make the problem tractable, in my lab, a rat model is used to elucidate how cortical neural activities lead to conscious, goal-directed movement and control. However we allow rats to freely move about in the experimental apparatus so to capture their natural movement and mental conditions. This talk discusses findings based on single unit, multi-channel simultaneous recordings from ratâ??s frontal areas while they learned to perform a decision and control task. By exploring the neural activities from the ratâ??s cortical regions, we developed and utilized analytical techniques to uncover the interactions between neurons at different time scales. The findings provide interesting neural substrate to ratâ??s learning control behavior. The work involves both experimental and computational studies. In the experiment, rats were placed in a Skinner box for a self-paced lever pressing task that they learned by trial and error. The goal of the task is to switch a sided light cue to a center location from one of five locations. The movement of the light can be controlled by the rat with the press of either a left or a right lever. Our computational modeling reveals neural adaptation as rats learned to master the task. Our results entail both high level statistical snapshots of the neural data and more detailed dynamic modeling with functional synaptic efficacies to capture before and after learning neural characteristics and their relationships to behavior. While performing the analyses, we aimed at providing mecahstic account of how brains generate meaningful behaviors under our designed experimental condition using biologically plausible computational models.

  • Published on
  • July 12, 2012

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