Decoding Neuronal Ensembles from Spatially-Referenced Calcium Traces: A Bayesian Semiparametric Approach

TL;DR

This paper presents a Bayesian semiparametric model to decode neuronal ensembles from calcium imaging data, jointly inferring spikes and functional groupings, revealing spatially structured co-activation patterns in hippocampal neurons.

Contribution

The paper introduces a novel Bayesian approach that jointly infers neuronal spiking and ensembles from calcium traces, incorporating spatial information and heterogeneity filtering.

Findings

Uncovered spatially structured neuronal co-activation patterns.

Demonstrated the model's ability to identify functionally coherent ensembles.

Revealed how ensemble structures vary with animal position.

Abstract

Understanding how neurons coordinate their activity is a fundamental question in neuroscience, with implications for learning, memory, and neurological disorders. Calcium imaging has emerged as a powerful method to observe large-scale neuronal activity in freely moving animals, providing time-resolved recordings of hundreds of neurons. However, fluorescence signals are noisy and only indirectly reflect underlying spikes of neuronal activity, complicating the extraction of reliable patterns of neuronal coordination. We introduce a fully Bayesian, semiparametric model that jointly infers spiking activity and identifies functionally coherent neuronal ensembles from calcium traces. Our approach models each neuron's spiking probability through a latent Gaussian process and encourages anatomically coherent clustering using a location-dependent stick-breaking prior. A spike-and-slab Dirichlet process captures heterogeneity in spike amplitudes while filtering out negligible events. We consider calcium imaging data from the hippocampal CA1 region of a mouse as it navigates a circular arena, a setting critical for understanding spatial memory and neuronal representation of environments. Our model uncovers spatially structured co-activation patterns among neurons and can be employed to reveal how ensemble structures vary with the animal's position.