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

2025-08-13

AI Summary

This study addresses the challenge of decoding coordinated neuronal activation patterns and identifying functionally coherent neuronal ensembles from spatially referenced calcium imaging data recorded in freely behaving mice’s hippocampal CA1 region, to elucidate neural coding mechanisms underlying spatial memory. We propose a Bayesian semiparametric joint modeling framework that integrates an anatomically informed, location-dependent sticky-breaking prior with a spike-and-slab Dirichlet process—enabling simultaneous functional ensemble identification under structural constraints and heterogeneous spike amplitude modeling. A latent Gaussian process captures spatially modulated firing probabilities, and efficient posterior inference is performed via MCMC. Our method successfully uncovers spatially structured coactivation ensembles whose composition dynamically evolves with animal position. The resulting interpretable and generalizable statistical paradigm advances understanding of distributed, cooperative neural computation during natural behavior.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Bayesian Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Decoding noisy calcium traces to infer neuronal spiking activity
• Identifying functionally coherent neuronal ensembles from imaging data
• Modeling spatially structured co-activation patterns in hippocampal neurons

Innovation

• Bayesian semiparametric model for neuronal decoding
• Latent Gaussian process for spiking probability
• Location-dependent prior for anatomical coherence