Nonlinear Integrate-and-Fire Model

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

Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.

Explores firing thresholds in 2D neuron models, pulse input, delayed spike initiation, and the FitzHugh-Nagumo model.

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Explores the firing threshold in the Hodgkin-Huxley model and the behavior of neurons under different current inputs.

Explores stochastic spike arrival in single neurons and the impact of network noise.

Explores the biophysical understanding of neuronal behavior, focusing on action potentials, neuron modeling challenges, and dendritic inhibition.

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

Covers the Generalized Linear Model (GLM) in computational neuroscience.

Explores the dynamics of neuronal populations, emphasizing random networks and mean-field arguments for connectivity.