TY  - GEN
ID  - cogprints2380
UR  - http://cogprints.org/2380/
A1  - Spratling, M. W.
A1  - Johnson, M. H.
Y1  - 2002///
N2  - A large and influential class of neural network architectures use
post-integration lateral inhibition as a mechanism for competition. We argue
that these algorithms are computationally deficient in that they fail to
generate, or learn, appropriate perceptual representations under certain
circumstances. An alternative neural network architecture is presented in which
nodes compete for the right to receive inputs rather than for the right to
generate outputs. This form of competition, implemented through pre-integration
lateral inhibition, does provide appropriate coding properties and can be used
to efficiently learn such representations. Furthermore, this architecture is
consistent with both neuro-anatomical and neuro-physiological data. We thus
argue that pre-integration lateral inhibition has computational advantages over
conventional neural network architectures while remaining equally biologically
plausible.
TI  - Pre-integration lateral inhibition enhances unsupervised learning
SP  - 2157
AV  - public
EP  - 2179
ER  -

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