This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
The Virtual Brain (TVB) is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to TVB in particular. This lesson also presents the newest approaches for clinical applications of TVB - that is, for stroke, epilepsy, brain tumors, and Alzheimer’s disease - and show how brain simulation can improve diagnostics, therapy, and understanding of neurological disease.
This lesson explains the mathematics of neural mass models and their integration to a coupled network. You will also learn about bifurcation analysis, an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Lastly, the application of the described mathematics is demonstrated in the exploration of brain stimulation regimes.
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.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This opening lecture from INCF's Short Course in Neuroinformatics provides an overview of the field of neuroinformatics itself, as well as laying out an argument for the necessity for developing more sophisticated approaches towards FAIR data management principles in neuroscience.
This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects.
This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.
Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
Presented by the OHBM OpenScienceSIG, this lesson covers how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers.
Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.
This compilation is courtesy of freeCodeCamp.
This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.
This lesson was created by RootMath.
This lecture covers the history of behaviorism and the ultimate challenge to behaviorism.
This lecture covers various learning theories.
This lecture provides an introduction to the principal of anatomical organization of neural systems in the human brain and spinal cord that mediate sensation, integrate signals, and motivate behavior.
This lecture focuses on the comprehension of nociception and pain sensation, highlighting how the somatosensory system and different molecular partners are involved in nociception.
This module covers some basic anatomy such as the brain’s major divisions (brainstem, cerebellum, cerebrum), the cerebral lobes (frontal, temporal, parietal, and occipital), the central and peripheral nervous systems, theories of cognition, and brain orientation terms.
This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling.
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.