  Studies of insect olfactory processing indicate that odors are represented by
rich spatio-temporal patterns of neural activity. These patterns are very
difficult to predict a priori, yet they are stimulus specific and reliable upon
repeated stimulation with the same input. We formulate here a theoretical
framework in which we can interpret these experimental results. We propose a
paradigm of ``dynamic competition'' in which inputs (odors) are represented by
internally competing neural assemblies. Each pattern is the result of dynamical
motion within the network and does not involve a ``winner'' among competing
possibilities. The model produces spatio-temporal patterns with strong
resemblance to those observed experimentally and possesses many of the general
features one desires for pattern classifiers: large information capacity,
reliability, specific responses to specific inputs, and reduced sensitivity to
initial conditions or influence of noise. This form of neural processing may
thus describe the organizational principles of neural information processing in
sensory systems and go well beyond the observations on insect olfactory
processing which motivated its development.
