This lesson provides an introduction to modeling single neurons, as well as stability analysis of neural models.
This lesson continues a thorough description of the concepts, theories, and methods involved in the modeling of single neurons.
In this lesson you will learn about fundamental neural phenomena such as oscillations and bursting, and the effects these have on cortical networks.
This lesson continues discussing properties of neural oscillations and networks.
In this lecture, you will learn about rules governing coupled oscillators, neural synchrony in networks, and theoretical assumptions underlying current understanding.
This lesson provides a continued discussion and characterization of coupled oscillators.
This lesson gives an overview of modeling neurons based on firing rate.
This lesson characterizes the pattern generation observed in visual system hallucinations.
This lesson gives an introduction to stability analysis of neural models.
This lesson continues from the previous lectures, providing introduction to stability analysis of neural models.
In this lesson, you will learn about phenomena of neural populations such as synchrony, oscillations, and bursting.
This lesson continues from the previous lecture, giving an overview of various neural phenomena such as oscillations and bursting.
This lesson provides more context around weakly coupled oscillators.
This lesson builds upon previous lectures in this series, providing an overview of coupled oscillators.
In this lesson, you will learn about neuronal models based on their spike rate.
In this lesson, you will learn about neural activity pattern generation in visual system hallucinations.
This lecture covers NeuronUnit, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support validating single-neuron models using data gathered by neurophysiologists.
This lesson provides an introduction to the NeuroElectro project, which aims to organize information on cellular neurophysiology.
This lesson covers simultaneously recorded neurons in non-human primates coordinate their spiking activity in a sequential manner that mirrors the dominant wave propagation directions of the local field potentials.
This talk covers statistical analysis of spike train data, the modeling approach GLM, and the problem of assessing neural synchrony.