TY - JOUR T1 - Identification of multiple noise sources improves estimation of neural responses across stimulus conditions JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0191-21.2021 SP - ENEURO.0191-21.2021 AU - Alison I. Weber AU - Eric Shea-Brown AU - Fred Rieke Y1 - 2021/06/01 UR - http://www.eneuro.org/content/early/2021/06/02/ENEURO.0191-21.2021.abstract N2 - Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions.Significance StatementCurrent models for neural responses typically focus on accurately estimating a neuron’s average response to a stimulus but often fail to accurately reflect response variability. Such variability is central to the accuracy with which neural responses represent inputs and with which they can guide behavior. We present a new modeling framework that accurately captures observed variability in neural responses and find that multiple stochastic model elements are necessary to capture this variability. We show that model parameters can be accurately estimated using about 8 minutes of data. We then apply the model to retinal ganglion cells, demonstrating light level-dependent changes in both deterministic and stochastic model elements changes that are either obscured or absent using more standard modeling approaches. ER -