Likelihood of Spike Train in GLM

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Models and data

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

Parameter estimation

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

Spike Response Model (SRM)

Covers the Spike Response Model (SRM) in computational neuroscience and its relation to the adaptive leaky integrate-and-fire model.

Modeling in vitro data

Explores modeling in vitro data for computational neuroscience, including predicting subthreshold voltage and spike times.

In Silico Neuroscience: Ion Channels and Neuronal Models

Explores detailed modeling of ion channels and neuronal morphologies in in silico neuroscience, covering neuron classification, ion channel kinetics, and experimental observations.

Scientific Computing in Neuroscience

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

Generalized Integrate-and-Fire Models

Explores the Generalized Integrate-and-Fire Model and the Nonlinear Integrate-and-Fire Model.

AdEx: Adaptive exponential integrate-and-fire

Covers the AdEx model, firing patterns, adaptation, and parameter estimation.

Escape noise

Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.

Helping Humans: Neuroprosthetics

Explores the application of computational neuroscience in neuroprosthetics, focusing on predicting intended arm movements based on spike times and the importance of systematic parameter optimization.