ArticleScientific reports2021
A convolutional neural network for estimating synaptic connectivity from spike trains.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains.PLoS computational biology · 2026Article
- Comparing effective and functional connectivity.Scientific reports · 2026Article
- Deriving connectivity from spiking activity in detailed models of large-scale cortical microcircuits.iScience · 2026Article
- Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy.PLoS computational biology · 2025Article
- Advances in large-scale electrophysiology with high-density microelectrode arrays.Lab on a chip · 2025Review
- Experience-dependent reorganization of inhibitory neuron synaptic connectivity.bioRxiv : the preprint server for biology · 2025Article
- Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons.Frontiers in computational neuroscience · 2025Article
- Neural Correlates of Category Learning in Monkey Inferior Temporal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Nondifferentiable activity in the brain.PNAS nexus · 2024Article
- Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains.PLoS computational biology · 2024Article
- Neural correlates of category learning in monkey inferior temporal cortex.bioRxiv : the preprint server for biology · 2023Article
- Deconvolution improves the detection and quantification of spike transmission gain from spike trains.Communications biology · 2022Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
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Authors and funding
9 authors.
Funding
Abstract
The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.
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