ArticlePatterns (New York, N.Y.)2023
Compressive sensing of functional connectivity maps from patterned optogenetic stimulation of neuronal ensembles.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computational optimization of two-photon holographic stimulation sitesJournal of neural engineering · 2026Article
- Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing.Nature neuroscience · 2025Article
- High-throughput synaptic connectivity mapping using in vivo two-photon holographic optogenetics and compressive sensing.Nature neuroscience · 2025Article
- Multiphoton Neurophotonics: Recent Advances in Imaging and Manipulating Neuronal Circuits.ACS photonics · 2025Review
- Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.Journal of neural engineering · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Mapping functional connectivity between neurons is an essential step toward probing the neural computations mediating behavior. Accurately determining synaptic connectivity maps in populations of neurons is challenging in terms of yield, accuracy, and experimental time. Here, we developed a compressive sensing approach to reconstruct synaptic connectivity maps based on random two-photon cell-targeted optogenetic stimulation and membrane voltage readout of many putative postsynaptic neurons. Using a biophysical network model of interconnected populations of excitatory and inhibitory neurons, we characterized mapping recall and precision as a function of network observability, sparsity, number of neurons stimulated, off-target stimulation, synaptic reliability, propagation latency, and network topology. We found that mapping can be achieved with far fewer measurements than the standard pairwise sequential approach, with network sparsity and synaptic reliability serving as primary determinants of the performance. Our results suggest a rapid and efficient method to reconstruct functional connectivity of sparsely connected neuronal networks.
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