Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.
Covers the optimization of neuron models for coding and decoding in computational neuroscience.
Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.
Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.
Explores modeling in vitro data for computational neuroscience, including predicting subthreshold voltage and spike times.
Explores different types of noise in electrical systems and their impact on electronic devices.
Compares diffusive noise and escape noise models in computational neuroscience, discussing simulation, calculation, and model fitting.
Covers noise analysis in circuits, including small-signal and input-referred noise, with examples of amplifiers and filters.
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Explores electrical metrology, covering random variables, noise sources, and their impact on electronic devices.
Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.
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.
