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 will highlight our current understanding and recent developments in the field of neurodegenerative disease research, as well as the future of diagnostics and treatment of neurodegenerative diseases
How genetics can contribute to our understanding of psychiatric phenotypes.
2nd part of the lecture. Introduction to cell receptors and signalling cascades
Introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.
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.