Abstract
Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper, we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.
Highlights
IntroductionA foundational idea in sensory neuroscience is that the activity of neural populations constitutes a “neural code” for representing stimuli (Dayan and Abbott, 2005; Doya, 2007): the activity pattern of a population in response to a sensory stimulus encodes information about that stimulus, and downstream neurons decode, process, and re-encode this information in their own responses
A foundational idea in sensory neuroscience is that the activity of neural populations constitutes a “neural code” for representing stimuli (Dayan and Abbott, 2005; Doya, 2007): the activity pattern of a population in response to a sensory stimulus encodes information about that stimulus, and downstream neurons decode, process, and re-encode this information in their own responses.Sequences of such neural populations implement the elementary functions that drive perception, cognition, and behaviour (Pitkow and Angelaki, 2017)
The two-parameter CoM-Poisson model contains the one-parameter Poisson model as a special case, whereas the Poisson model always has a Fano factor (FF; the variance divided by the mean) of 1, the CoM-Poisson model can exhibit both over- (FF>1) and underdispersion, and capture the broader range of Fano factors observed in cortex (Stevenson, 2016)
Summary
A foundational idea in sensory neuroscience is that the activity of neural populations constitutes a “neural code” for representing stimuli (Dayan and Abbott, 2005; Doya, 2007): the activity pattern of a population in response to a sensory stimulus encodes information about that stimulus, and downstream neurons decode, process, and re-encode this information in their own responses. Sequences of such neural populations implement the elementary functions that drive perception, cognition, and behaviour (Pitkow and Angelaki, 2017). The correlations between neurons’ responses to repeated presentations of a given stimulus (noise correlations), and how these noise correlations are modulated by stimuli, can strongly impact coding in neural circuits (Zohary et al, 1994; Abbott and Dayan, 1999; Sompolinsky et al, 2001; Ecker et al, 2016; Kohn et al, 2016; Schneidman, 2016), especially in large populations of neurons (Moreno-Bote et al, 2014; Montijn et al, 2019; Bartolo et al, 2020; Kafashan et al, 2021; Rumyantsev et al, 2020)
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