This lecture provides an introduction to the study of eye-tracking in humans. 

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 the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

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 talk enumerates the challenges regarding data accessibility and reusability inherent in the current scientific publication system, and discusses novel approaches to these challenges, such as the EBRAINS Live Papers platform. 

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 provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

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.