#neuralmanifolds — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #neuralmanifolds, aggregated by home.social.
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🧠 New paper by Pezon, Schmutz & Gerstner: Linking #NeuralManifolds to circuit structure in recurrent networks.
The study connects two common views of neural activity: low-dimensional #PopulationDynamics (“neural manifolds”) and single-neuron selectivity. Using recurrent network models, the authors show how circuit connectivity constrains both the geometry of neural #manifolds and the tuning of individual neurons.
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🧠 New paper by Pezon, Schmutz & Gerstner: Linking #NeuralManifolds to circuit structure in recurrent networks.
The study connects two common views of neural activity: low-dimensional #PopulationDynamics (“neural manifolds”) and single-neuron selectivity. Using recurrent network models, the authors show how circuit connectivity constrains both the geometry of neural #manifolds and the tuning of individual neurons.
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🧠 New paper by Pezon, Schmutz & Gerstner: Linking #NeuralManifolds to circuit structure in recurrent networks.
The study connects two common views of neural activity: low-dimensional #PopulationDynamics (“neural manifolds”) and single-neuron selectivity. Using recurrent network models, the authors show how circuit connectivity constrains both the geometry of neural #manifolds and the tuning of individual neurons.
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🧠 New paper by Pezon, Schmutz & Gerstner: Linking #NeuralManifolds to circuit structure in recurrent networks.
The study connects two common views of neural activity: low-dimensional #PopulationDynamics (“neural manifolds”) and single-neuron selectivity. Using recurrent network models, the authors show how circuit connectivity constrains both the geometry of neural #manifolds and the tuning of individual neurons.
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🧠 New paper by Pezon, Schmutz & Gerstner: Linking #NeuralManifolds to circuit structure in recurrent networks.
The study connects two common views of neural activity: low-dimensional #PopulationDynamics (“neural manifolds”) and single-neuron selectivity. Using recurrent network models, the authors show how circuit connectivity constrains both the geometry of neural #manifolds and the tuning of individual neurons.
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Cool work on conserved #MotorCortex dynamics across species. #Behavior differs mainly through different trajectories on shared #NeuralManifolds. #NeuralDynamics #CompNeuro #Neuroscience 🧪
RE: https://bsky.app/profile/did:plc:tfffyrbltg3reliv5wq35on3/post/3mgpw73yhac2q -
There's a great talk by Juan Gallego on how low-dimensional #NeuralManifolds arise from biological constraints, remain invariant across states and inputs, and support cross-animal alignment. Examples span #HeadDirection rings, #gridcell tori, #MotorCortex prep vs movement, striatal timing dynamics, and C. elegans #behavior loops. Cool talk as it shows how #manifold-level structure can generalize across tasks and organisms.
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There's a great talk by Juan Gallego on how low-dimensional #NeuralManifolds arise from biological constraints, remain invariant across states and inputs, and support cross-animal alignment. Examples span #HeadDirection rings, #gridcell tori, #MotorCortex prep vs movement, striatal timing dynamics, and C. elegans #behavior loops. Cool talk as it shows how #manifold-level structure can generalize across tasks and organisms.
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There's a great talk by Juan Gallego on how low-dimensional #NeuralManifolds arise from biological constraints, remain invariant across states and inputs, and support cross-animal alignment. Examples span #HeadDirection rings, #gridcell tori, #MotorCortex prep vs movement, striatal timing dynamics, and C. elegans #behavior loops. Cool talk as it shows how #manifold-level structure can generalize across tasks and organisms.
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There's a great talk by Juan Gallego on how low-dimensional #NeuralManifolds arise from biological constraints, remain invariant across states and inputs, and support cross-animal alignment. Examples span #HeadDirection rings, #gridcell tori, #MotorCortex prep vs movement, striatal timing dynamics, and C. elegans #behavior loops. Cool talk as it shows how #manifold-level structure can generalize across tasks and organisms.
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There's a great talk by Juan Gallego on how low-dimensional #NeuralManifolds arise from biological constraints, remain invariant across states and inputs, and support cross-animal alignment. Examples span #HeadDirection rings, #gridcell tori, #MotorCortex prep vs movement, striatal timing dynamics, and C. elegans #behavior loops. Cool talk as it shows how #manifold-level structure can generalize across tasks and organisms.
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📚 New Nat Rev Neurosci #JournalClub by @juangallego: Neural #manifolds: more than the sum of their neurons. He reflects on the shift from single-neuron mappings to population-level #ManifoldRepresentations and suggests that neural manifolds might capture fundamental principles of neural computation and do not just serve as interpretative tools 👍
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@axoaxonic Indeed! Here, for everyone else, is the link to the article I originally posted by mistake:
🌍 https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(24)00119-0
📝 Scott, Daniel N. et al. , Thalamocortical architectures for flexible cognition and efficient learning, 2024, Trends in Cognitive Sciences, Volume 28, Issue 8, 739 - 756 -
In their study, Morales-Gregorio et al. show that #NeuralManifolds in #V1 shift dynamically under top-down influence from #V4. They identify two distinct population activity states – eyes open vs. closed – with notably stronger V4→V1 signaling in the foveal region during eyes-open periods. A cool example of how cognitive context reshapes visual cortical dynamics.
🌍 https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5
#CompNeuro #Neuroscience #VisualCortex #NeuralManifolds #SystemsNeuroscience
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@juangallego just published a review on how #NeuralManifolds go beyond being a convenient data representation – they reflect fundamental constraints on #NeuralPopulation activity. Originating in mammalian BCI work (2014), these low-dimensional trajectories shape what neural patterns are learnable and expressible.
🌍 https://www.nature.com/articles/s41583-025-00919-0.epdf
#CompNeuro #SystemsNeuroscience #PopulationDynamics #Neuroscience
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Neural manifold analysis of brain circuit dynamics in health and disease. Mitchell-Heggs, Prado et al, JCNS 2022.
https://link.springer.com/article/10.1007/s10827-022-00839-3