This lecture provides an introduction to the course "Cognitive Science & Psychology: Mind, Brain, and Behavior".

This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging. 

This lecture focuses on the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.

This module explains how neurons come together to create the networks that give rise to our thoughts. The totality of our neurons and their connection is called our connectome. Learn how this connectome changes as we learn, and computes information.

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 lesson gives an introduction to simple spiking neuron models.

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.