Computational Neuroscience: Models and Theory

Topics covered in this lesson

Introduction to David Marr's work on levels on description/analysis of the brain as a complex system - computation, algorithm & representation, implementation. Marr's argument that neuroscience needs to start with studying the specific computation a system is trying to achieve, and his postulation that we need to study specific computations rather than "grand theories of the brain". Different flavors of models: 

• descriptive models (what) - often aim to capture a core phenomenon of interest, not all details 
• mechanistic models (how) - how is X implemented at a biophysical level? Challenge to find right level of abstraction. 
• interpretive models (why) - why is the system set up a particular way? What is its role/function?

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/