Continuation of coupled oscillators

Firing rate models.

Pattern generation in visual system hallucinations.

Introduction to the role of models in theoretical neuroscience

Different types of models, model complexity, and how to choose an appropriate model.

Balanced E-I networks, stability and gain modulation

Methods for dimensionality reduction of data, with focus on factor analysis.

Methods for dimensionality reduction of data, with focus on factor analysis.

Spiking neuron networks and linear response models.

Bayesian neuron models and parameter estimation.

Bayesian memory and learning, how to go from observations to latent variables.

Constraints can help us understand how the brain works.

Approaching neural systems from an evolutionary perspective

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

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.

Introduction to simple spiking neuron models.

The ionic basis of the action potential, including the Hodgkin Huxley model. 

Forms of plasticity on many levels - short-term, long-term, metaplasticity, structural plasticity. With examples related to modelling of biochemical networks. 

[NB: The sound uptake is a bit noisy the first few minutes, but gets better from about 5 mins in]

Introduction to modelling of chemical computation in the brain