Attractor Networks: Hopfield Model Generalizations

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Discusses memory encoding principles and techniques to enhance retention and recall.

Covers modeling electrophysiology at different scales, discussing ion channels, single neurons, and microcircuits.

Covers the optimization of neuron models for coding and decoding in computational neuroscience.

Explores the concept of separation of time scales in computational neuroscience and the reduction of detail in two-dimensional neuron models.

Explores the AdEx neuron model, analyzing firing patterns and phase planes.

Examines the effects of stress on learning and the importance of teacher-student relationships on engagement and achievement.

Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.

Explores Spike-Timing Models of plasticity, including STDP and synaptic traces.

Discusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.

Explores the Leaky Integrate-and-Fire Model in computational neuroscience, emphasizing single neuron dynamics.