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:
๐ https://www.fabriziomusacchio.com/blog/2026-02-12-stdp/
#CompNeuro #Neuroscience #SNN #NeuralDynamics #NeuralPlasticity
Finally published with David Angulo Garcia:
A theory for self-sustained balanced states in absence of strong external currents
@PLOS
Dynamical 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
๐ง 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.
#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:
๐https://www.fabriziomusacchio.com/blog/2026-02-04-neural_dynamics/
๐ง 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:
๐ง 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.
Whatโs 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).
๐ https://arxiv.org/abs/2511.22828
๐ป https://github.com/CMC-lab/fastDSA
#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
๐ง 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.
๐ง 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.
๐ง 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.
๐ง New preprint by Codol et al. (2025): Brain-like #NeuralDynamics for #behavioral control develop through #ReinforcementLearning. They show that only #RL, not #SupervisedLearning, yields neural activity geometries & dynamics matching monkey #MotorCortex recordings. RL-trained #RNNs operate at the edge of #chaos, reproduce adaptive reorganization under #visuomotor rotation, and require realistic limb #biomechanics to achieve brain-like control.
Fabrizio Musacchio
Alessandro Torcini
Fabrizio Musacchio