Pattern association and retrieval in a continuous neural system | Zendy

Hung-Jen Chang | Zendy; Joydeep Ghosh | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Pattern association and retrieval in a continuous neural system

Author(s) -

Hung-Jen Chang,

Joydeep Ghosh

Publication year - 1993

Publication title -

biological cybernetics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.608

H-Index - 95

eISSN - 1432-0770

pISSN - 0340-1200

DOI - 10.1007/bf00201410

Subject(s) - invariant (physics) , nonlinear system , visual cortex , scaling , associative property , content addressable memory , artificial neural network , computer science , complex system , translation (biology) , artificial intelligence , mathematics , algorithm , pattern recognition (psychology) , physics , pure mathematics , geometry , neuroscience , biochemistry , chemistry , quantum mechanics , messenger rna , gene , mathematical physics , biology

This paper studies the behavior of a large body of neurons in the continuum limit. A mathematical characterization of such systems is obtained by approximating the inverse input-output nonlinearity of a cell (or an assembly of cells) by three adjustable linearized sections. The associative spatio-temporal patterns for storage in the neural system are obtained by using approaches analogous to solving space-time field equations in physics. A noise-reducing equation is also derived from this neural model. In addition, conditions that make a noisy pattern retrievable are identified. Based on these analyses, a visual cortex model is proposed and an exact characterization of the patterns that are storable in this cortex is obtained. Furthermore, we show that this model achieves pattern association that is invariant to scaling, translation, rotation and mirror-reflection.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research