10.1371/journal.pcbi.1005150 Braden A. W. Brinkman Braden A. W. Brinkman Alison I. Weber Alison I. Weber Fred Rieke Fred Rieke Eric Shea-Brown Eric Shea-Brown How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits? Public Library of Science 2016 noise source encoding strategies Previous work Neural Circuits noise sources encode inputs strategy processing impacts Efficient Coding Strategies Depend coding hypothesis coding strategies circuit nonlinearities adaptational changes encoding properties Neural circuits 2016-10-14 17:45:38 Dataset https://plos.figshare.com/articles/dataset/How_Do_Efficient_Coding_Strategies_Depend_on_Origins_of_Noise_in_Neural_Circuits_/4035666 <div><p>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.</p></div>