Cart (Loading....) | Create Account
Close category search window
 

Using energy efficiency to make sense out of neural information processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Levy, W.B. ; Dept. of Neurosurg., Virginia Univ., Charlottesville, VA, USA ; Baxter, R.A.

One of the earliest information theoretic interpretations of neural processing and of cognitive processing is recoding to remove statistical dependency while avoiding or minimizing information loss by such a transformation. Since that time there have been many articles that use entropies and relative entropies (including mutual information) to describe various aspects of information in the brain. However, there are two widespread and well known neurobiological observations that cannot be explained by information measures alone. First, on average binary neuron signaling occurs an order of magnitude below the information optimizing rate. Second, there is a natural form of randomization that is by far the greatest source of noise in neocortical neurons called "quantal synaptic failures". Consideration of information measures in the context of energy efficiency explains both of these observations. From the viewpoint of energy consumption, neural processing is rather expensive. The adult human brain accounts for 20% or more of our total energy use, and in young children energy use by the brain can account for nearly 50% of the caloric intake. Thus, there is compelling motivation to hypothesize that microscopic parameterizations of the nervous system are optimized for energy use.

Published in:

Information Theory, 2002. Proceedings. 2002 IEEE International Symposium on

Date of Conference:

2002

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.