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Neurons can display highly variable dynamics. While such variabilitypresumably supports the wide range of behaviors generated by the organism,their gene expressions are relatively stable in the adult brain. This suggeststhat neuronal activity is a combination of its time-invariant identity and theinputs the neuron receives from the rest of the circuit. Here, we propose aself-supervised learning based method to assign time-invariant representationsto individual neurons based on permutation-, and population size-invariantsummary of population recordings. We fit dynamical models to neuronal activityto learn a representation by considering the activity of both the individualand the neighboring population. Our self-supervised approach and use ofimplicit representations enable robust inference against imperfections such aspartial overlap of neurons across sessions, trial-to-trial variability, andlimited availability of molecular (transcriptomic) labels for downstreamsupervised tasks. We demonstrate our method on a public multimodal dataset ofmouse cortical neuronal activity and transcriptomic labels. We report > 35%improvement in predicting the transcriptomic subclass identity and > 20%improvement in predicting class identity with respect to the state-of-the-art.

Learning Time-Invariant Representations for Individual Neurons from Population Dynamics