recurrent network of perceptrons with three state synapsesachieves competitive classification on real inputs
Clicks: 238
ID: 169330
2012
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
30.0
/100
238 views
33 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #74 of 128 articles by views in population health management
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 128 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
We describe an attractor network of binary perceptrons receiving inputs from a retinotopicvisual feature layer. Each class is represented by a random subpopulation of the attractor layer,which is turned on in a supervised manner during learning of the feed forward connections. Theseare discrete three state synapses and are updated based on a simple field dependent Hebbian rule.For testing, the attractor layer is initialized by the feedforward inputs and then undergoes asynchronousrandom updating until convergence to a stable state. Classification is indicated by thesub-population that is persistently activated. The contribution of this paper is twofold. First,this is the first example of competitive classification rates of real data being achieved throughrecurrent dynamics in the attractor layer, which is only stable if recurrent inhibition is introduced.Second, we demonstrate that employing three state synapses with feedforward inhibition is essentialfor achieving the competitive classification rates due to the ability to effectively employboth positive and negative informative features.
| Reference Key |
eamit2012frontiersrecurrent
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Yali eAmit;Jacob eWalker |
| Journal | population health management |
| Year | 2012 |
| DOI |
10.3389/fncom.2012.00039
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.