Reading the cortical code for natural motion
Type of Award: catalyst
Award Period: February 2016 - January 2018
Amount Awarded: $ 199,555.00
PI(s): Jason MacLean, UChicago; Stephanie Palmer, UChicago; David Schwab, NU;
Abstract: Discovering the language the brain uses to represent the world is a difficult challenge. Currently, our best models are unable to explain 90% of the activity of the visual area of the brain when viewing natural scenes. The current theoretical framework for visual processing in the brain is based on static models that neglect neuron-neuron interactions. Our work takes into account the connected and dynamic nature of real neural activity and makes use of new experimental and computational techniques to improve our model of the brain's code for motion. In this proposal, we aim to uncover the set of neural activity patterns that are preferentially driven by salient visual input, such as the trajectory of a threatening predator, as opposed to background motion, like the fluttering of leaves in the wind, to optimally predict the future trajectory of moving objects in the world - a key computation performed in neocortex.