Compares diffusive noise and escape noise models in computational neuroscience, discussing simulation, calculation, and model fitting.
Explores the Poisson model in computational neuroscience, emphasizing rate coding and stochastic spiking.
Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.
Covers the Spike Response Model (SRM) in computational neuroscience and its relation to the adaptive leaky integrate-and-fire model.
Discusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.
Covers the optimization of neuron models for coding and decoding in computational neuroscience.
Explores the quality of Integrate-and-Fire models in computational neuroscience through comparisons with experimental data and mathematical predictions.
Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.
Explores electrical metrology, covering random variables, noise sources, and their impact on electronic devices.
Explores modeling in vitro data for computational neuroscience, including predicting subthreshold voltage and spike times.
