Lecture on the most important concepts in software engineering

This lecture gives an introduction to simulation, models, and the neural simulation tool NEST. 

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture covers describing and characterizing an input-output relationship.

This lecture covers structured data, databases, federating neuroscience-relevant databases, ontologies. 

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, 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

Part 1 of 2 of a tutorial on statistical models for neural data

Part 2 of 2 of a tutorial on statistical models for neural data.

Introduction to simple spiking neuron models.

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, 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

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.