Kristine Heiney: Relating representational drift to neural circuit interactions

Abstract

In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. In this talk, I will first discuss how a neuron’s tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. In this analysis, we quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed ‘redundant’, and information requiring knowledge of the state of both neurons is termed ‘synergistic’. We found that a neuron's tuning stability is positively correlated with the strength of its average pairwise redundancy with the population, and that these high-redundancy neurons also tend to show high average pairwise synergy. We hypothesise that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. At the end of the talk, I will briefly discuss ongoing work on modelling representational drift in excitatory–inhibitory rate-based circuit models, building on our findings from this data analysis.  

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