Math Biology Seminar: Braden Brinkman

Our sensory nervous system receives vast quantities of external information that it must reliably encode and transmit to deeper regions of the brain. These signals can become corrupted by noise at various stages of transmission, and yet our brain is able to reliably decode this sensory information and perform computations with it. For example, we are able to see over a wide range of light levels from daylight to starlight, despite the drastic change in relative noise levels as photon rates decrease. Sensory neurons are able to adjust how they respond to stimuli to account for changes in the environment. How then should neural encoding strategies be adjusted so as to be robust to different sources of noise throughout the circuit? We develop a simple neural circuit model to solve for these optimal encoding strategies, focusing on neurons arranged in parallel channels. We find that noise sources entering the circuit at different processing states compete to determine the optimal encoding strategy, including whether pathways should encode common stimuli independently or redundantly, and whether pairs of neurons have the same or opposite sensitivity to stimuli.