ArticleNature neuroscience2022
A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.
Article in Nature neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Organoid intelligence: a promising paradigm for autism spectrum disorder research.Molecular psychiatry · 2026Review
- Article
- Evaluating linear model frameworks for directed predictive influence estimation from hypothalamic calcium imaging.Journal of neuroscience methods · 2026Article
- Mouse sensorimotor cortex reflects complex kinematic details during reaching and grasping.eLife · 2026Article
- Technical development of two-photon optogenetic stimulation and its potential application to brain-machine interfaces.Neurophotonics · 2026Review
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Dissecting neuronal circuit function and dysfunction in Alzheimer's disease mouse models: from conventional calcium imaging analysis to machine learning-based approaches.Frontiers in aging neuroscience · 2026Review
- Improved inference of latent neural states from calcium imaging data.bioRxiv : the preprint server for biology · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.ArXiv · 2025Article
- Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.bioRxiv : the preprint server for biology · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.Proceedings of machine learning research · 2025Article
- Unraveling the neural code: analysis of large-scale two-photon microscopy data.Microscopy (Oxford, England) · 2025Review
- Article
- High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model.bioRxiv : the preprint server for biology · 2025Article
- Article
- MARBLE: interpretable representations of neural population dynamics using geometric deep learning.Nature methods · 2025Article
- POCO: Scalable Neural Forecasting through Population Conditioning.Advances in neural information processing systems · 2025Article
- Innovating beyond electrophysiology through multimodal neural interfaces.Nature reviews electrical engineering · 2025Article
- Interpretable deep learning for deconvolutional analysis of neural signals.bioRxiv : the preprint server for biology · 2024Article
- Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings.Scientific reports · 2024Article
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8 authors.
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Abstract
In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.
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