In this lesson, the simulation of a virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved clinical results. You will learn about the fundamentals of epilepsy, as well as the concepts underlying epilepsy simulation. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.
In this lesson you will learn how to simulate seizure events and epilepsy in The Virtual Brain. We will look at the paper On the Nature of Seizure Dynamics, which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor. In this part, we focus on setting up the parameters.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This brief talk goes into work being done at The Alan Turing Institute to solve real-world challenges and democratize computer vision methods to support interdisciplinary and international researchers.
This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects.
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 an Introduction to neuron anatomy and signaling, and different types of models, 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 is part 1 of 2 of a tutorial on statistical models for neural data.
This lesson is part 2 of 2 of a tutorial on statistical models for neural data.
This lecture covers an Introduction to neuron anatomy and signaling, as well as different types of models, including the Hodgkin-Huxley model.
This lecture describes forms of plasticity on many levels: short-term, long-term, metaplasticity, and structural plasticity. Included in this lecture are also examples related to modelling of biochemical networks.
This lesson provides an introduction to modelling of chemical computation in the brain.
This lesson provides an introduction to the role of models in theoretical neuroscience, particularly focusing on David Marr's work on levels of description/analysis of the brain as a complex system: computation, algorithm & representation, and implementation.
In this lesson, you will learn about different types of models, model complexity, and how to choose an appropriate model.
This lesson provides an overview of balanced excitatory-inhibitory (E-I) networks, stability, and gain modulation.
This lesson introduces methods for dimensionality reduction of data, with focus on factor analysis.
This lecture delves into the dynamics of neural computation, from the spiking activity of single neurons to regional cortical population coding and network activity.