Comparison of Noise ModelsCompares diffusive noise and escape noise models in computational neuroscience, discussing simulation, calculation, and model fitting.
Poisson Model: Rate CodingExplores the Poisson model in computational neuroscience, emphasizing rate coding and stochastic spiking.
Likelihood of a spike trainDiscusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.
Spike Response Model (SRM)Covers the Spike Response Model (SRM) in computational neuroscience and its relation to the adaptive leaky integrate-and-fire model.
Three definitions of rate codeDiscusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.
Supervised Learning EssentialsIntroduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Parameter estimationExplores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.
