# Learning spatially-correlated temporal dictionaries for calcium imaging

## TL;DR

This paper introduces a novel dictionary learning approach for calcium imaging data that emphasizes spatial inference and temporal trace detection, improving neuron activity recovery with hierarchical spatial modeling.

## Contribution

The paper proposes a new spatially-correlated temporal dictionary learning method that enhances neuron activity detection and inference in calcium imaging data, with hierarchical spatial filtering.

## Key findings

- Improves initialization robustness in neuron detection.
- Implicitly infers the number of neurons.
- Detects different neuronal types simultaneously.

## Abstract

Calcium imaging has become a fundamental neural imaging technique, aiming to recover the individual activity of hundreds of neurons in a cortical region. Current methods (mostly matrix factorization) are aimed at detecting neurons in the field-of-view and then inferring the corresponding time-traces. In this paper, we reverse the modeling and instead aim to minimize the spatial inference, while focusing on finding the set of temporal traces present in the data. We reframe the problem in a dictionary learning setting, where the dictionary contains the time-traces and the sparse coefficient are spatial maps. We adapt dictionary learning to calcium imaging by introducing constraints on the norms and correlations of the time-traces, and incorporating a hierarchical spatial filtering model that correlates the time-trace usage over the field-of-view. We demonstrate on synthetic and real data that our solution has advantages regarding initialization, implicitly inferring number of neurons and simultaneously detecting different neuronal types.