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 provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.
This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect.
In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience.
This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
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 lesson covers the ionic basis of the action potential, 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 gives a presentation on computationally demanding studies of synaptic plasticity on the molecular level.
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 lesson provides an introduction to the myriad forms of cellular mechanisms whicn underpin healthy brain function and communication.
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.