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.
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#Neuroscience #Hippocampus #SpatialNavigation #NeuralDynamics
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
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#Neuroscience #Hippocampus #SpatialNavigation #NeuralDynamics
🧠⭕️ 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
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
🧠🎨 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.
@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.
🧠 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.
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
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.
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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.
Fabrizio Musacchio