ArticleNature neuroscience2020
Systematic errors in connectivity inferred from activity in strongly recurrent networks.
Article in Nature neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Inferring effective neuronal circuits via network flux counting.PLoS computational biology · 2026Article
- Inferring brain-wide interactions using data-constrained recurrent neural network models.Neuron · 2026Article
- Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains.PLoS computational biology · 2026Article
- Resolving non-identifiability mitigates systematic errors in simultaneous models of neural tuning and functional coupling.Journal of computational neuroscience · 2026Article
- Data-Constrained Recurrent Network Neural Model Uncovers the Circuit Mechanism of Olfactory OFF Responses.bioRxiv : the preprint server for biology · 2026Article
- Simple input-output dependencies explain neuronal activity.Nature physics · 2026Article
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- A topographical organization in the primary olfactory cortex.Nature communications · 2026Article
- Prediction of neural activity in connectome-constrained recurrent networks.Nature neuroscience · 2025Article
- Constraining inference of across-region interactions using neural activity perturbations.bioRxiv : the preprint server for biology · 2025Article
- No Central Executive? Decision Formation Through Multi-Area Population Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Review
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- Advances in large-scale electrophysiology with high-density microelectrode arrays.Lab on a chip · 2025Review
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- Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures.bioRxiv : the preprint server for biology · 2025Article
- Mapping effective connectivity by virtually perturbing a surrogate brain.Nature methods · 2025Article
- Neural populations are dynamic but constrained.Nature neuroscience · 2025Article
- Experience-dependent reorganization of inhibitory neuron synaptic connectivity.bioRxiv : the preprint server for biology · 2025Article
- Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks.Advances in neural information processing systems · 2025Article
- Functional connectivity drifts during sleep as a marker of fluctuations in the level of consciousness.Neuroscience of consciousness · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been an interest in estimating them algorithmically from multicell activity recordings. We show that even sophisticated methods, applied to unlimited data from every cell in the circuit, are biased toward inferring connections between unconnected but highly correlated neurons. This failure to 'explain away' connections occurs when there is a mismatch between the true network dynamics and the model used for inference, which is inevitable when modeling the real world. Thus, causal inference suffers when variables are highly correlated, and activity-based estimates of connectivity should be treated with special caution in strongly connected networks. Finally, performing inference on the activity of circuits pushed far out of equilibrium by a simple low-dimensional suppressive drive might ameliorate inference bias.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.