Ly, Cheng

Marsat, Gary

English

VCU Scholars Compass

Virginia Commonwealth UniversityVCU Scholars CompassBiology and Medicine Through MathematicsConference 2017May 19th, 4:30 PM - 5:00 PMFiring Rate Heterogeneity and Consequences forCoding in Feedforward CircuitsCheng LyVirginia Commonwealth University, cly@vcu.eduGary MarsatWest Virginia University, gary.marsat@wvu.eduFollow this and additional works at: http://scholarscompass.vcu.edu/bammPart of the Life Sciences Commons, Medicine and Health Sciences Commons, and the StatisticalModels CommonsThis Event is brought to you for free and open access by the Dept. of Mathematics and Applied Mathematics at VCU Scholars Compass. It has beenaccepted for inclusion in Biology and Medicine Through Mathematics Conference by an authorized administrator of VCU Scholars Compass. Formore information, please contact libcompass@vcu.edu.http://scholarscompass.vcu.edu/bamm/2017/friday/15Firing Rate Heterogeneity and Consequences for Coding inFeedforward CircuitsCheng Ly∗Virginia Commonwealth UniversityRichmond, VA 23284CLy@vcu.eduGary MarsatWest Virginia Universitygary.marsat@wvu.eduKeywords: firing rate heterogeneity, electric fish, neural coding.AbstractHeterogeneity of neural attributes is recognized as a crucial feature in neural processing.We present recently developed theoretical methods to characterize how the firing rate distri-bution of neurons changes with intrinsics and network heterogeneity in a generic recurrentspiking neural network model. The relationship between intrinsic and network heterogene-ity can lead to various levels of firing rate heterogeneity, depending on regime. We employdimension reduction methods and asymptotic analysis to derive compact expressions to de-scribe the phenomena.Next we adapt our work to a delayed feedforward network model of the electrosensory sys-tem of electric fish. Experimental recordings indicate that feedforward network input canmediate response heterogeneity of pyramidal cells. We use the theory to demonstrate struc-tured connectivity rules can lead to qualitatively similar statistics as the experimental data.The stimulus tuning of particular cells is related to the effective network architecture orconnectivity. Thus, the model demonstrates that intrinsic and network attributes do notinteract in a linear manner but rather in a complex stimulus-dependent fashion to increaseor decrease neural heterogeneity and thus shape population codes.We also present some preliminary work based on electric fish data with noisy stimuli wherewe find that firing rate heterogeneity is a signature of optimal (Bayesian) stimulus estimation.

Firing Rate Heterogeneity and Consequences for Coding in Feedforward Circuits

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1170&amp;context=bamm

Firing Rate Heterogeneity and Consequences for Coding in Feedforward Circuits

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