# Integration of Calcium Imaging Traces via Deep Generative Modeling

arXiv: https://arxiv.org/abs/2501.14615 · HTML: https://mlsanity.com/paper/2501.14615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [Learning Disentangled Representations with Semi-Supervised Deep Generative Models](https://mlsanity.com/paper/1706.00400/) (2017)
- [Gaussian Process Prior Variational Autoencoders](https://mlsanity.com/paper/1810.11738/) (2018)
- [Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE](https://mlsanity.com/paper/2011.04798/) (2020)
- [Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity](https://mlsanity.com/paper/2107.04084/) (2021)
- [Fast Online Deconvolution of Calcium Imaging Data](https://mlsanity.com/paper/1609.00639/) (2016)
- [Fast amortized inference of neural activity from calcium imaging data with variational autoencoders](https://mlsanity.com/paper/1711.01846/) (2017)
- [Role of connectivity anisotropies in the dynamics of cultured neuronal networks](https://mlsanity.com/paper/2501.04427/) (2025)

## Abstract (arXiv)

Calcium imaging allows for the parallel measurement of large neuronal populations in a spatially resolved and minimally invasive manner, and has become a gold-standard for neuronal functionality. While deep generative models have been successfully applied to study the activity of neuronal ensembles, their potential for learning single-neuron representations from calcium imaging fluorescence traces remains largely unexplored, and batch effects remain an important hurdle. To address this, we explore supervised variational autoencoder architectures that learn compact representations of individual neurons from fluorescent traces without relying on spike inference algorithms. We find that this approach outperforms state-of-the-art models, preserving biological variability while mitigating batch effects. Across simulated and experimental datasets, this framework enables robust visualization, clustering, and interpretation of single-neuron dynamics.