Computational Overview
Computational Overview
This chapter provides an overview of computational principles that may be useful when addressing the question of computation in nervous systems as well as questions of biological systems. It begins by introducing several key mathematical concepts, including “function,” and the distinction between computable and noncomputable functions, and between linear and nonlinear functions. It then considers a number of computational principles, such as the look-up table and linear associators, before discussing a new type of principle that can accomplish the satisfaction of constraints by a process of “relaxation.” In particular, it describes Hopfield networks and Boltzmann machines. It also examines learning in neural nets, competitive learning, curve fitting, feedforward nets, and recurrent nets. Finally, it assesses the importance of optimization procedures to neuroscience, along with the use of realistic and abstract network models in neuroscience.
Keywords: computational principles, computation, nervous systems, function, look-up table, linear associators, constraints, learning, neural nets, network models
MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
Please, subscribe or login to access full text content.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.