preprints available on Web

[Sebastian Seung]

The following preprints can be found in
http://portal.research.bell-labs.com/home/seung@physics/papers/

How the brain keeps the eyes still
H. S. Seung
To appear in PNAS

The brain can hold the eyes still because it stores a memory of eye
position.  The brain's memory of horizontal eye position appears to be
represented by persistent neural activity in a network known as the
neural integrator, which is localized in the brainstem and cerebellum.
Existing experimental data are reinterpreted as evidence for an
attractor hypothesis, that the persistent patterns of activity
observed in this network form an attractive line of fixed points in
its state space.  Line attractor dynamics can be produced in linear or
nonlinear neural networks by learning mechanisms that precisely tune
positive feedback.


Unsupervised learning by convex and conic encoding
D. D. Lee and H. S. Seung
To appear in NIPS

Unsupervised learning algorithms based on convex and conic encoders
are proposed.  The encoders find the closest convex or conic
combination of basis vectors to the input.  The learning algorithms
produce basis vectors that minimize the reconstruction error of the
encoders.  The convex algorithm develops locally linear models of the
input, while the conic algorithm discovers features.  Both algorithms
are used to model handwritten digits and compared with vector
quantization and principal component analysis.  The neural network
implementations involve lateral connections, which mediate cooperative
and competitive interactions and allow for the development of sparse
distributed representations.


Statistical mechanics of Vapnik-Chervonenkis entropy
P. Riegler and H. S. Seung

A statistical mechanics of learning is formulated in terms of a Gibbs
distribution on the realizable labelings of a set of inputs.  The
entropy of this distribution is a generalization of the
Vapnik-Chervonenkis (VC) entropy, reducing to it exactly in the limit
of infinite temperature.  Perceptron learning of randomly labeled
patterns is analyzed within this formalism.