This lecture covers an introduction to neuroinformatics and its subfields, the content of the short course and future neuroinformatics applications.
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 structured data, databases, federating neuroscience-relevant databases, ontologies.
How genetics can contribute to our understanding of psychiatric phenotypes.
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
Conference presentation on computationally demanding studies of synaptic plasticity on the molecular level
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.
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
Conference presentation on computationally demanding studies of synaptic plasticity on the molecular level
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.