#neuraldynamics — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #neuraldynamics, aggregated by home.social.
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RE: https://mastodon.social/@ViolaPriesemann/117132315819947046
Check out the speakers lineup! 👌👇
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If you are interested, I wrote a simplified SCA-like #Python script to get the idea of #SCA compared to #PCA:
👨💻 https://github.com/fabriziomusacchio/SCA_demo
📚 https://www.fabriziomusacchio.com/blog/2026-07-19-sca/#a-simplified-python-implementation -
We had #JournalClub today and I presented Zimnik et al.’s recent paper on #SparseComponentAnalysis (#SCA), a novel method for finding sparse, interpretable latent factors in #NeuralPopulation activity. Since not everything fits into a single JC, I wrote a detailed summary and discussion of the paper on my blog. Feel free to share and comment your thoughts:
🌍 https://www.fabriziomusacchio.com/blog/2026-07-19-sca/
📄 https://doi.org/10.1016/j.neuron.2026.05.022
👨💻 https://github.com/glaserlab/sca -
@virati
#neuraldynamics #compneuroscience
#neuroscience #compneuro #brain #neuralnetwork
#neurodon @hnp_genevaA new article with Alejandro Carballosa on the dimension of neural data
A detailed investigation of Shared Variance Component Analysis as a tool to characterize neural dimensionality
Journal of Neuroscience Methods
#Principal Component Analysis
#Shared Variance Component Analysis
# Neural data
#Spontaneous activity -
Modeling suggests that sparse #CA3 input can speed up #learning of new #SpatialMaps, while dense #CA1 coding provides a more efficient, compressed representation for large-scale #navigation.
🧵2/2
#Neuroscience #Hippocampus #SpatialNavigation #NeuralDynamics
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New paper by Maimon et al (Ulanovsky lab): recordings from #bats flying through tunnels up to 200 m reveal a sparse-to-dense transformation between #hippocampal #CA3 and #CA1.
In small environments, CA3 and CA1 #PlaceCells look similar. At large spatial scales, however, CA3 #neurons mostly show single, ultrasparse #PlaceFields, while CA1 neurons show dense multifield coding.
🌍 https://doi.org/10.1038/s41586-026-10537-0
🧵1/2
#Neuroscience #Hippocampus #SpatialNavigation #NeuralDynamics
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🧠⭕️ New preprint by Hulse et al: How can the #fly’s neural compass show #RingAttractor dynamics despite heterogeneous #connectomic wiring? They combine task-trained #RNNs, theory and fly #connectomes to show that “dynamical clones” can embed hidden symmetries in apparently messy connectivity, preserving stable head-direction #attractors and angular velocity integration.
🌍 https://doi.org/10.64898/2026.05.18.725766
#Neuroscience #CompNeuro #NeuralDynamics #NeuralManifolds #RingAttractor
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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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@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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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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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 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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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 -
🧠 New work by Codol et al. who show that #MotorCortex dynamics are remarkably conserved across #mice, #monkeys, and #humans. Despite very different #behaviors, #NeuralPopulation activity follows similar dynamical rules on low-dimensional #manifolds. Species differences arise mainly from the geometry of trajectories within this shared #DynamicalSystem.
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🧠 New preprint by Chericoni et al: #NeuralPopulation activity in the #hippocampus encodes spatial information for different agents (self, prey, gaze) in distinct but related low-dimensional subspaces. The study shows that these representations can be linearly transformed between each other, suggesting a shared geometric code supporting flexible spatial #cognition and multi-agent navigation.
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Spike-timing-dependent #plasticity (#STDP) is a core rule in #ComputationalNeuroscience that adjusts #synaptic strength based on precise pre- vs. postsynaptic #spike timing, enabling #TemporalCoding and #learning in #SNN. In this post, I summarize its mathematical formulation, functional consequences for learning and #memory along with a simple #Python example:
🌍 https://www.fabriziomusacchio.com/blog/2026-02-12-stdp/
#CompNeuro #Neuroscience #SNN #NeuralDynamics #NeuralPlasticity
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Finally published with David Angulo Garcia:
A theory for self-sustained balanced states in absence of strong external currents
@PLOSDynamical balance can be obtained via nonlinear mechanisms without the need of strong external drive: short term depression act as a new balancing mechanism. The complete theory is here reported.
#neuraldynamics #compneuroscience
#neuroscience #compneuro #brain #neuralnetwork
#neurodon @hnp_geneva -
🧠 New paper by Tafazoli et al. (2026): The authors show that the brain reuses the same lowdim #NeuralSubspaces across tasks. #Sensory features & #MotorActions are encoded in shared population #subspaces, & task switching occurs by flexibly engaging & transforming activity between these representations. Monkeys adapt by updating internal task beliefs & routing activity through the appropriate shared subspaces.
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#NeuralDynamics is a central subfield of #ComputationalNeuroscience studying timedependent #NeuralActivity and its governing #mathematics. It examines how #NeuralStates evolve, how stable or unstable patterns arise, and how #learning reshapes them. Neural dynamics forms the backbone for how #neurons & #NeuralNetworks generate complex activity over time. This post gives a brief overview of the field & its historical milestones:
🌍https://www.fabriziomusacchio.com/blog/2026-02-04-neural_dynamics/
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🧠 New preprint by Shervani-Tabar, Brincat & @ekmiller on emergent #TravelingWaves in #RNN.
By aligning RNN dynamics to an empirically measured #NeuralManifold, they show that task-relevant TW can emerge through #learning, w/o hard-coding wave dynamics or connectivity. The cool thing here is that the waves are not imposed or engineered, but emerge naturally from learning under #BiologicallyPlausible constraints:
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Lee et al.: Fast dendritic excitations primarily mediate #backpropagation in #CA1 pyramidal #neurons during #behavior
Using kHz #VoltageImaging across the full #dendritic tree, they show that fast dendritic spikes are usually driven by somatic #bAPs, not independently initiated. #bAP propagation into apical dendrites is contin. modulated by pre-spike dendritic voltage & can trigger slower plateau potentials linked to complex spikes.
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🧠 New paper by Deistler et al: #JAXLEY: differentiable #simulation for large-scale training of detailed #biophysical #models of #NeuralDynamics.
They present a #differentiable #GPU accelerated #simulator that trains #morphologically detailed biophysical #neuron models with #GradientDescent. JAXLEY fits intracellular #voltage and #calcium data, scales to 1000s of compartments, trains biophys. #RNNs on #WorkingMemory tasks & even solves #MNIST.
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🧠 New #preprint by Komi et al. (2025): Neural #manifolds that orchestrate walking and stopping. Using #Neuropixels recordings from the lumbar spinal cord of freely walking rats, they show that #locomotion arises from rotational #PopulationDynamics within a low-dimensional limit-cycle #manifold. When walking stops, the dynamics collapse into a postural manifold of stable fixed points, each encoding a distinct pose.
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📚 New preprint by Song et al.: Geometry of #NeuralDynamics along the #cortical #attractor landscape reflects changes in attention. They show that while attractor positions are determined by cortical organization, the geometry of neural dynamics on the landscape changes systematically with attentional states and contexts.
🌍 https://www.biorxiv.org/content/10.1101/2025.08.08.669432v1
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Eviatar Yemini will talk Todayw about "A Tale of Two Sexes: The #NeuralDynamics of #Dimorphic #Behavior"
⏰ September 18, 2023, 11 am CET
📍 #iBehave seminar series, #MPI Lecture Hall (at #Caesar), Ludwig-Erhard-Allee 2, 53175 Bonn / online
🌍 https://ibehave.nrw/news-and-events/ibehave-seminar-series-talk-by-prof-dr-eviatar-yemini/