Oscillations and bursting
Weakly coupled oscillators
Continuation of coupled oscillators
Firing rate models.
Pattern generation in visual system hallucinations.
This lecture covers computational principles that growth cones employ to detect and respond to environmental chemotactic gradients, focusing particularly on growth cone shape dynamics.
In this lecture you will learn that in developing mouse somatosensory cortex, endogenous Btbd3 translocate to the cell nucleus in response to neuronal activity and oriented primary dendrites toward active axons in the barrel hollow.
In this presentation, the speaker describes some of their recent efforts to characterize the transcriptome of the developing human brain, and and introduction to the BrainSpan project.
Introduction to OpenWorm: an open source project dedicated to creating a virtual C. elegans nematode in a computer.
This lecture covers the aims of the Open Source Brain initiative, the current functionality of the website and the range of models already available, and future plans for the project.
This lecture covers NeuronUnit, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support validating single-neuron models using data gathered by neurophysiologists.
An introduction to the NeuroElectro project, which aims to organize information on cellular neurophysiology. Speaker: Shreejoy Tripathy
In this lecture, the speaker demonstrates Neurokernel's module interfacing feature by using it to integrate independently developed models of olfactory and vision LPUs based upon experimentally obtained connectivity information.
Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.
Brain network reconstruction from empirical data is of key importance to generate personalized virtual brain models. This lecture will introduce the basic concepts of preprocessing structural, functional and diffusion weighted neuroimages. It highlights the latest methods and pipelines to extract structural as well as functional connectomes according to a multimodal parcellation.
Along the example of a patient with bi-temporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. The patient's brain network model is then evaluated via simulation, data fitting and mathematical analysis. This lecture demonstrates how to develop novel personalized strategies towards therapy and intervention using TVB.
This lecture focuses on higher-level simulation scenarios using stimulation protocols. We demonstrate how to build stimulation patterns in TVB, and use them in a simulation to induced activity dissipating into experimentally known resting-state networks in human and mouse brain, a well as to obtain EEG recordings reproducing empirical findings of other researchers.
This lecture presents two recent clinical case studies using TVB: stroke recovery and dementia (due to Alzheimer’s Disease (AD)). Using a multi-scale neurophysiological model based on empirical multi-modal neuroimaging data, we show how local and global biophysical parameters characterize changes in individualized patient-specific brain dynamics, predict recovery of motor function for stroke patients, and correlate with individual differences in cognition for AD patients.
In this lesson, Yaroslav O. Halchenko describes how DataLad allows you to track and mange both your data and analysis code, thereby facilitating reliable, reproducible, and shareable research.
Tutorial on how to simulate brain tumor brains with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls. Authors: Hannelore Aerts, Michael Schirner, Ben Jeurissen, DIrk Van Roost, Eric Achten, Petra Ritter, Daniele Marinazzo