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Fabrizio Musacchio
Fabrizio Musacchio
@FabMusacchio@mastodon.social  ·  activity timestamp 4 days ago

The Urbanczik-Senn plasticity model is a powerful framework for understanding synaptic #plasticity in #NeuralNetworks. It integrates dendritic prediction errors to unify supervised, unsupervised, and #ReinforcementLearning under a single rule. Its predictive coding mechanism and robust learning dynamics make it valuable for simulating neural processing and exploring plasticity. Here’s a short simulation using the #NESTsimulator:

🌍 https://www.fabriziomusacchio.com/blog/2026-02-22-urbanczik_senn_plasticity/

#CompNeuro #Neuroscience

2 media
Evolutions of synaptic weights of Urbanczik synapses.
Evolutions of synaptic weights of Urbanczik synapses.
Evolutions of synaptic weights of Urbanczik synapses.
Urbanczik-Senn plasticity : membrane potentials, somatic conductances, dendritic currents, fireing rates, and rate derivative.
Urbanczik-Senn plasticity : membrane potentials, somatic conductances, dendritic currents, fireing rates, and rate derivative.
Urbanczik-Senn plasticity : membrane potentials, somatic conductances, dendritic currents, fireing rates, and rate derivative.
Fabrizio Musacchio

Urbanczik-Senn plasticity

The Urbanczik-Senn plasticity model proposes a learning rule for dendritic synapses in a simplified compartmental neuron model. This rule extends traditional spike-timing-dependent plasticity (STDP) by incorporating the local dendritic potential as a crucial third factor, alongside pre- and postsynaptic spike timings. In this post, we briefly introduce the Urbanczik-Senn plasticity model and discuss its implications for neural computation and learning.
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