Abstract
Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.
Highlights
Much is known about how single neurons encode information about stimuli, how neurons contribute to reported percepts is less well understood[1]
We begin in section Decoding framework with some core definitions for neural population responses and estimation tasks based on decoding from multiple populations
In the section Analysis of choice correlations, we describe the expected patterns of choice-related activity under the assumptions of optimal and suboptimal decoding. These patterns depend on the structure of neural noise, so in the section, Models of neural variability, we describe two fundamentally different noise models, whose information content is extensive or limited
Summary
Much is known about how single neurons encode information about stimuli, how neurons contribute to reported percepts is less well understood[1]. Experimenters selectively activate or inactivate brain regions of interest, and measure resulting perceptual or behavioural changes. If CCs reflect a functional link between neurons and behaviour, one would expect brain areas with greater CCs to contribute more strongly to behaviour This naïve view is contradicted by recent results that reveal a striking dissociation between the magnitude of CCs and the effects of inactivation across brain systems in rodents[16,17] and primates[18,19]. This apparent disagreement is not all that surprising because the two techniques, on their own, yield results whose interpretation is fraught with major difficulties
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