Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.
Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.
Explores electronic, thermomechanical, and amplifier noise, calibration of amplitude, frequency tracking, and system limits.
Compares diffusive noise and escape noise models in computational neuroscience, discussing simulation, calculation, and model fitting.
Explores electrical metrology, covering random variables, noise sources, and their impact on electronic devices.
Discusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.
Compares rate codes and temporal codes in neural dynamics, emphasizing timing codes, stochastic resonance, and neural coding challenges.
Introduces the fundamentals of noise in electronics, covering origins, signal types, power characteristics, and noise sources.
Explores different types of noise in electrical systems and their impact on electronic devices.
Covers the Spike Response Model (SRM) in computational neuroscience and its relation to the adaptive leaky integrate-and-fire model.
Explores the factors influencing the annoyance level of noise, such as loudness, spectrum, and temporal features.
Explores the quality of Integrate-and-Fire models in computational neuroscience through comparisons with experimental data and mathematical predictions.
Explores the variability of spike trains in computational neuroscience, covering experiments, sources of variability, and stochastic spike arrival and firing.
