Computational Neuroscience: The Basics

Source: INCF TrainingSpace
 

This introductory course provides those interested in computational neuroscience with an overview of the sub-specialisms within computational neuroscience.

Lessons in This Course

 

1. Introduction to Modeling the Brain

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

2. Some Simple Models of Neurons

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

3. The Action Potential

The ionic basis of the action potential, including the Hodgkin Huxley model.

4. Modeling Across Scales of Analysis

Forms of plasticity on many levels - short-term, long-term, metaplasticity, structural plasticity. With examples related to modeling of biochemical networks.

[NB: The sound uptake is a bit noisy the first few minutes, but gets better from about 5 mins in.]

5. Principles of Intracellular Modeling and Computation

Introduction to modeling of chemical computation in the brain.

6. Simulating the Long Time Scales and Large Molecule Numbers Involved in Synaptic Plasticity

Conference presentation on computationally demanding studies of synaptic plasticity on the molecular level.


TrainingSpace (TS) is an online hub that aims to make neuroscience educational materials more accessible to the global neuroscience community. As a hub, TS provides users with access to:

  • Multimedia educational content from courses, conference lectures, and laboratory exercises from some of the world’s leading neuroscience institutes and societies.
  • Study tracks to facilitate self-guided study.
  • Tutorials on tools and open science resources for neuroscience research.
  • A Q&A forum.
  • A neuroscience encyclopedia that provides users with access to over 1,000,000 publicly available datasets as well as links to literature references and scientific abstracts.

Topics currently included in TS include: general neuroscience, clinical neuroscience, computational neuroscience, neuroinformatics, computer science, data science, and open science.

All courses and conference lectures in TS include a general description, topics covered, links to prerequisite courses if applicable, and links to software described in or required for the course, as well as links to the next lecture in the course or more advanced related courses.

To learn more about TrainingSpace, visit: https://training.incf.org/

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