ArticleNature neuroscience2025
Prediction of neural activity in connectome-constrained recurrent networks.
Article in Nature neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Range-Limited Scale Stability of Dynamic Direction Representations in a Connectome-Constrained Fly Visual Model.Neuroinformatics · 2026Article
- Pretraining of Embodied Recurrent Networks Bridges the Gap Between Artificial and Cortical Neural Activities.Biomimetics (Basel, Switzerland) · 2026Article
- Data-Constrained Recurrent Network Neural Model Uncovers the Circuit Mechanism of Olfactory OFF Responses.bioRxiv : the preprint server for biology · 2026Article
- Analysis and dynamic modeling of firing synchronization in electrically interconnected dual-compartment neuronal networks.Microsystems & nanoengineering · 2026Article
- Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia.NPJ systems biology and applications · 2026Article
- The Electromechanical Connectome: Integrating Voltage, Mechanical Nano-Forces, and Subcellular Fluid Phase Dynamics in Human Neural Computation.International journal of molecular sciences · 2026Review
- Grand challenges for systems neuroscience: perspectives and opportunities.Frontiers in systems neuroscience · 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
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Abstract
Recent technological advances have enabled measurement of the synaptic wiring diagram, or 'connectome', of large neural circuits or entire brains. However, the extent to which such data constrain models of neural dynamics and function is debated. In this study, we developed a theory of connectome-constrained neural networks in which a 'student' network is trained to reproduce the activity of a ground truth 'teacher', representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, the two networks have the same synaptic weights but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We found that a connectome often does not substantially constrain the dynamics of recurrent networks, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory demonstrates that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and determines when activity in such networks can be well predicted. It can also prioritize which neurons to record to most effectively inform such predictions.
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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.