title: How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?
abstract: Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.
authors.affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.|Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America.
authors.forename: Braden A W
authors.identifier: http://orcid.org/0000-0002-2572-8861
authors.initials: BA
authors.lastname: Brinkman
authors.affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.|Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America.|Graduate Program in Neuroscience, University of Washington, Seattle, Washington, United States of America.
authors.forename: Alison I
authors.initials: AI
authors.lastname: Weber
authors.affiliation: Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America.|Graduate Program in Neuroscience, University of Washington, Seattle, Washington, United States of America.|Howard Hughes Medical Institute, University of Washington, Seattle, Washington, United States of America.
authors.forename: Fred
authors.identifier: http://orcid.org/0000-0002-1052-2609
authors.initials: F
authors.lastname: Rieke
authors.affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.|Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America.|Graduate Program in Neuroscience, University of Washington, Seattle, Washington, United States of America.
authors.forename: Eric
authors.initials: E
authors.lastname: Shea-Brown
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D011984:Sensory Receptor Cells / Q000502:physiology*; D059629:Signal-To-Noise Ratio; D009435:Synaptic Transmission / Q000502:physiology*