This course consists of a series of lessons which aim to introduce the basic conceptual and experimental approaches in computational neuroscience.
Computational Neuroscience: The Basics
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.
This lesson covers the ionic basis of the action potential, including the Hodgkin-Huxley model.
This lesson discuses forms of neural plasticity on many levels, including short-term, long-term, metaplasticity, and structural plasticity. During the lesson you will also be presented with examples related to the modelling of biochemical networks.
This lesson provides an introduction to modelling of chemical computation in the brain.
This lesson gives a presentation on computationally demanding studies of synaptic plasticity on the molecular level.