Distributed recognition codes, catastrophic forgetting, and the rules of synaptic transmission

Gail A. Carpenter

doi:10.1117/12.188902

10 October 1994 Distributed recognition codes, catastrophic forgetting, and the rules of synaptic transmission

Gail A. Carpenter

Proceedings Volume 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision; (1994) https://doi.org/10.1117/12.188902
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

Analysis of catastrophic forgetting by distributed codes leads to the unexpected conclusion that the standard synaptic transmission rule may not be optimal in certain neural networks. The distributed outstar generalizes the outstar network for spatial pattern learning, replacing the outstar source node with a source field, of arbitrarily many nodes, where the activity pattern may be arbitrarily distributed or compressed. Distributed outstar learning proceeds according to a principle of atrophy due to disuse whereby a path weight decreases in joint proportion to the transmitted path signal and the degree of disuse of the target node. During learning, the total signal to target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals. Three types of synaptic transmission, the standard product rule, a fuzzy capacity rule, and an adaptive threshold rule, are examined for this system. Only the threshold rule solves the catastrophic forgetting problem of fast learning. Analysis of spatial distributed coding hereby leads to the conjecture that the unit of long-term memory is a spatial pattern learning system may be a subtractive threshold, rather than a multiplicative weight.

Citation Download Citation

Gail A. Carpenter "Distributed recognition codes, catastrophic forgetting, and the rules of synaptic transmission", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); https://doi.org/10.1117/12.188902

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Synaptic transmission

Neural networks

Digital filtering

Fuzzy logic

Cognitive modeling

Lead

Signal processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years