Automatically assigned DDC number: 00632

Manually assigned DDC number: 00632

Number of references: 16

Title: Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning

Author:

Subject: Randall C. O'reilly Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning

Description: Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Error-driven backpropagation is computationally powerful, and has proven useful for modeling a range of psychological data, but is not biologically plausible. Several approaches to implementing backpropagation in a biologically plausible fashion converge on the idea of using bidirectional activation propagation in interactive networks to convey error signals. This paper demonstrates two main points about these error-driven interactive networks: (a) they generalize poorly due to attractor dynamics that interfere with the network's ability to systematically produce novel combinatorial representations in response to novel inputs; and (b) this generalization problem can be remedied by adding two widely used mechanistic principles, inhibitory competition and Hebbian learning, that can be independent...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2000-10-13

Format: ps

Identifier: http://citeseer.ist.psu.edu/385568.html

Source: ftp://grey.colorado.edu/pub/oreilly/papers/oreilly00_gen_nc.ps

Language: en

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