Computational Neuroscience

Layer 3 — Biology18 concepts in this subtree

Theoretical / model-based neuroscience: neural-network dynamics, learning rules, dimensionality, coding theory, mean-field analysis, attractor networks. Distinct from cognitive-, behavioural-, systems-neuroscience trees.

Hopfield attractor network
Hodgkin-Huxley network dynamics
Efficient coding (Barlow-Attneave)
Population vector decoding (Georgopoulos)
Dimensionality reduction of neural data (PCA / dPCA / GPFA)
Random recurrent network chaos (Sompolinsky-Crisanti-Sommers)
Spike-timing-dependent plasticity (STDP)
Balanced network (van Vreeswijk-Sompolinsky)
Predictive coding (Rao-Ballard)
Reservoir computing (Jaeger / Maass)
Free-energy / active inference (Friston)
Ring attractor head-direction network
Hodgkin-Huxley (1952)
STDP (Markram-Bi-Poo 1997-1998)
Integrate-and-fire (Lapicque 1907)
Wilson-Cowan neural-mass (1972)
Efficient coding (Barlow 1961)
Predictive coding (Rao-Ballard 1999)
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