#neuraldynamics — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #neuraldynamics, aggregated by home.social.
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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Short-term #synaptic #plasticity (#STP) transiently modulates synaptic strength based on recent activity. #ShortTermDepression #STD reduces efficacy during repeated activity, while #ShortTermFacilitation #STF can enhance responses to closely spaced #spikes. These dynamics shape #NeuralProcessing, #filtering, and synaptic #homeostasis. Here's a short #Python implementation and simulation in #NESTSimulator:
🌍 https://www.fabriziomusacchio.com/blog/2026-05-25-std_and_stf/
#CompNeuro #Neuroscience #NeuralDynamics #TsodyksMarkramModel
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🧠🎨 New paper by Meyer et al: #astrocytic #sodium #homeostasis is not uniform. Using multiphoton #FLIM in #mouse #brain slices and #invivo, they show strong #cellular and #subcellular heterogeneity in astrocytic Na⁺ levels.
Processes contain more Na⁺ than somata, Na⁺ varies between #astrocyte branches, and distinct Na⁺/K⁺-ATPase subunit patterns help tune local K⁺ uptake and #glutamate-linked Na⁺ influx.
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🧠🎨 New paper by Meyer et al: #astrocytic #sodium #homeostasis is not uniform. Using multiphoton #FLIM in #mouse #brain slices and #invivo, they show strong #cellular and #subcellular heterogeneity in astrocytic Na⁺ levels.
Processes contain more Na⁺ than somata, Na⁺ varies between #astrocyte branches, and distinct Na⁺/K⁺-ATPase subunit patterns help tune local K⁺ uptake and #glutamate-linked Na⁺ influx.
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🧠🎨 New paper by Meyer et al: #astrocytic #sodium #homeostasis is not uniform. Using multiphoton #FLIM in #mouse #brain slices and #invivo, they show strong #cellular and #subcellular heterogeneity in astrocytic Na⁺ levels.
Processes contain more Na⁺ than somata, Na⁺ varies between #astrocyte branches, and distinct Na⁺/K⁺-ATPase subunit patterns help tune local K⁺ uptake and #glutamate-linked Na⁺ influx.
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🧠🎨 New paper by Meyer et al: #astrocytic #sodium #homeostasis is not uniform. Using multiphoton #FLIM in #mouse #brain slices and #invivo, they show strong #cellular and #subcellular heterogeneity in astrocytic Na⁺ levels.
Processes contain more Na⁺ than somata, Na⁺ varies between #astrocyte branches, and distinct Na⁺/K⁺-ATPase subunit patterns help tune local K⁺ uptake and #glutamate-linked Na⁺ influx.
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🧠🎨 New paper by Meyer et al: #astrocytic #sodium #homeostasis is not uniform. Using multiphoton #FLIM in #mouse #brain slices and #invivo, they show strong #cellular and #subcellular heterogeneity in astrocytic Na⁺ levels.
Processes contain more Na⁺ than somata, Na⁺ varies between #astrocyte branches, and distinct Na⁺/K⁺-ATPase subunit patterns help tune local K⁺ uptake and #glutamate-linked Na⁺ influx.
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@computingnature The idea is provocative: Spontaneous activity may reflect a useful "critical initialization" for biological networks, providing a dynamical scaffold for #memory and time-dependent #computation.
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@computingnature The idea is provocative: Spontaneous activity may reflect a useful "critical initialization" for biological networks, providing a dynamical scaffold for #memory and time-dependent #computation.
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@computingnature The idea is provocative: Spontaneous activity may reflect a useful "critical initialization" for biological networks, providing a dynamical scaffold for #memory and time-dependent #computation.
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@computingnature The idea is provocative: Spontaneous activity may reflect a useful "critical initialization" for biological networks, providing a dynamical scaffold for #memory and time-dependent #computation.
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@computingnature The idea is provocative: Spontaneous activity may reflect a useful "critical initialization" for biological networks, providing a dynamical scaffold for #memory and time-dependent #computation.
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🧠 New paper by Pachitariu … @computingnature: spontaneous brainwide activity in mice shows macroscopic coordination that resembles linear dynamics driven by a critically normalized random symmetric matrix.
#Cortical and brainwide recordings showed power-law variance spectra, slow global activity modes, and little rotational structure, unlike #CA1, which looked closer to an efficient, less correlated code.
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The #brain’s code seems to be in constant flux. #Neurons fire much more erratically than researchers thought. What does that mean for how the brain works?
🌍 https://www.nature.com/articles/d41586-026-01554-0 by Diana Kwon
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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🧠 New preprint by Lu et al: Recordings from the human #hippocampus and anterior cingulate #cortex during three distinct tasks reveal that #NeuralPopulation activity is not fully task-specific. About half of the low-dimensional #NeuralSubspace structure was shared across tasks, suggesting a stable population geometry that may support flexible #cognition across different #behaviors.
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🧠 New preprint by Lu et al: Recordings from the human #hippocampus and anterior cingulate #cortex during three distinct tasks reveal that #NeuralPopulation activity is not fully task-specific. About half of the low-dimensional #NeuralSubspace structure was shared across tasks, suggesting a stable population geometry that may support flexible #cognition across different #behaviors.
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🧠 New preprint by Lu et al: Recordings from the human #hippocampus and anterior cingulate #cortex during three distinct tasks reveal that #NeuralPopulation activity is not fully task-specific. About half of the low-dimensional #NeuralSubspace structure was shared across tasks, suggesting a stable population geometry that may support flexible #cognition across different #behaviors.
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🧠 New preprint by Lu et al: Recordings from the human #hippocampus and anterior cingulate #cortex during three distinct tasks reveal that #NeuralPopulation activity is not fully task-specific. About half of the low-dimensional #NeuralSubspace structure was shared across tasks, suggesting a stable population geometry that may support flexible #cognition across different #behaviors.
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🧠 New preprint by Lu et al: Recordings from the human #hippocampus and anterior cingulate #cortex during three distinct tasks reveal that #NeuralPopulation activity is not fully task-specific. About half of the low-dimensional #NeuralSubspace structure was shared across tasks, suggesting a stable population geometry that may support flexible #cognition across different #behaviors.
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RE: https://mastodon.social/@appassionato/116493374179009767
Indeed, an excellent recommendation: Tristram D. Wyatt’s “#AnimalBehaviour: A Very Short Introduction” is a useful reminder for #NaturalisticNeuroscience: #Behavior is not just output, but evolved action in ecological and social context. Tinbergen’s questions, costs, signals, conflict, cooperation. This is exactly the conceptual bridge we need between eg #NeuralDynamics and real-world behavior.
🌍 https://global.oup.com/academic/product/animal-behaviour-9780198712152
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RE: https://mastodon.social/@appassionato/116493374179009767
Indeed, an excellent recommendation: Tristram D. Wyatt’s “#AnimalBehaviour: A Very Short Introduction” is a useful reminder for #NaturalisticNeuroscience: #Behavior is not just output, but evolved action in ecological and social context. Tinbergen’s questions, costs, signals, conflict, cooperation. This is exactly the conceptual bridge we need between eg #NeuralDynamics and real-world behavior.
🌍 https://global.oup.com/academic/product/animal-behaviour-9780198712152
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RE: https://mastodon.social/@appassionato/116493374179009767
Indeed, an excellent recommendation: Tristram D. Wyatt’s “#AnimalBehaviour: A Very Short Introduction” is a useful reminder for #NaturalisticNeuroscience: #Behavior is not just output, but evolved action in ecological and social context. Tinbergen’s questions, costs, signals, conflict, cooperation. This is exactly the conceptual bridge we need between eg #NeuralDynamics and real-world behavior.
🌍 https://global.oup.com/academic/product/animal-behaviour-9780198712152
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RE: https://mastodon.social/@appassionato/116493374179009767
Indeed, an excellent recommendation: Tristram D. Wyatt’s “#AnimalBehaviour: A Very Short Introduction” is a useful reminder for #NaturalisticNeuroscience: #Behavior is not just output, but evolved action in ecological and social context. Tinbergen’s questions, costs, signals, conflict, cooperation. This is exactly the conceptual bridge we need between eg #NeuralDynamics and real-world behavior.
🌍 https://global.oup.com/academic/product/animal-behaviour-9780198712152
-
RE: https://mastodon.social/@appassionato/116493374179009767
Indeed, an excellent recommendation: Tristram D. Wyatt’s “#AnimalBehaviour: A Very Short Introduction” is a useful reminder for #NaturalisticNeuroscience: #Behavior is not just output, but evolved action in ecological and social context. Tinbergen’s questions, costs, signals, conflict, cooperation. This is exactly the conceptual bridge we need between eg #NeuralDynamics and real-world behavior.
🌍 https://global.oup.com/academic/product/animal-behaviour-9780198712152
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🧠 New preprint by Garcia-Garcia et al.: The authors show that cerebellar #GranuleCells do not simply expand #cortical activity into a high-dimensional code. Instead, they preserve low-dimensional cortical #manifold geometry while reorienting it across contexts. This rotation separates similar tasks, reduces interference, and supports flexible dual-task learning.
📄 https://www.biorxiv.org/content/10.64898/2026.03.03.709240v1.full.pdf
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🧠 New preprint by Garcia-Garcia et al.: The authors show that cerebellar #GranuleCells do not simply expand #cortical activity into a high-dimensional code. Instead, they preserve low-dimensional cortical #manifold geometry while reorienting it across contexts. This rotation separates similar tasks, reduces interference, and supports flexible dual-task learning.
📄 https://www.biorxiv.org/content/10.64898/2026.03.03.709240v1.full.pdf
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🧠 New preprint by Garcia-Garcia et al.: The authors show that cerebellar #GranuleCells do not simply expand #cortical activity into a high-dimensional code. Instead, they preserve low-dimensional cortical #manifold geometry while reorienting it across contexts. This rotation separates similar tasks, reduces interference, and supports flexible dual-task learning.
📄 https://www.biorxiv.org/content/10.64898/2026.03.03.709240v1.full.pdf
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🧠 New preprint by Garcia-Garcia et al.: The authors show that cerebellar #GranuleCells do not simply expand #cortical activity into a high-dimensional code. Instead, they preserve low-dimensional cortical #manifold geometry while reorienting it across contexts. This rotation separates similar tasks, reduces interference, and supports flexible dual-task learning.
📄 https://www.biorxiv.org/content/10.64898/2026.03.03.709240v1.full.pdf
-
🧠 New preprint by Garcia-Garcia et al.: The authors show that cerebellar #GranuleCells do not simply expand #cortical activity into a high-dimensional code. Instead, they preserve low-dimensional cortical #manifold geometry while reorienting it across contexts. This rotation separates similar tasks, reduces interference, and supports flexible dual-task learning.
📄 https://www.biorxiv.org/content/10.64898/2026.03.03.709240v1.full.pdf
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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.
-
🧠 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 preprint by Guardamagna et al.: Using large-scale recordings in #rat pups, the authors show that toroidal #manifolds in #MEC emerge by P10, before eye and ear opening, upright gait, and active exploration. Ring-like manifolds appear even earlier, by P9. External spatial experience seems to align these preconfigured internal maps only later, as pups begin to navigate.
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🧠 New preprint by Guardamagna et al.: Using large-scale recordings in #rat pups, the authors show that toroidal #manifolds in #MEC emerge by P10, before eye and ear opening, upright gait, and active exploration. Ring-like manifolds appear even earlier, by P9. External spatial experience seems to align these preconfigured internal maps only later, as pups begin to navigate.
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🧠 New preprint by Guardamagna et al.: Using large-scale recordings in #rat pups, the authors show that toroidal #manifolds in #MEC emerge by P10, before eye and ear opening, upright gait, and active exploration. Ring-like manifolds appear even earlier, by P9. External spatial experience seems to align these preconfigured internal maps only later, as pups begin to navigate.