Single-neuron deep generative model uncovers underlying physics of neuronal activity in Ca imaging data
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
Calcium imaging has become a powerful alternative to electrophysiology for
studying neuronal activity, offering spatial resolution and the ability to
measure large populations of neurons in a minimally invasive manner. This
technique has broad applications in neuroscience, neuroengineering, and
medicine, enabling researchers to explore the relationship between neuron
location and activity. Recent advancements in deep generative models (DGMs)
have facilitated the modeling of neuronal population dynamics, uncovering
latent representations that provide insights into behavior prediction and
neuronal variance. However, these models often rely on spike inference
algorithms and primarily focus on population-level dynamics, limiting their
applicability for single-neuron analyses. To address this gap, we propose a
novel framework for single-neuron representation learning using autoregressive
variational autoencoders (AVAEs). Our approach embeds individual neurons'
spatiotemporal signals into a reduced-dimensional space without the need for
spike inference algorithms. The AVAE excels over traditional linear methods by
generating more informative and discriminative latent representations,
improving tasks such as visualization, clustering, and the understanding of
neuronal activity. Additionally, the reconstruction performance of the AVAE
outperforms the state of the art, demonstrating its ability to accurately
recover the original fluorescence signal from the learned representation. Using
realistic simulations, we show that our model captures underlying physical
properties and connectivity patterns, enabling it to distinguish between
different firing and connectivity types. These findings position the AVAE as a
versatile and powerful tool for advancing single-neuron analysis and lays the
groundwork for future integration of multimodal single-cell datasets in
neuroscience.
