Local homeostasis stabilizes a model of the olfactory system globally in respect to perturbations by input during pattern classification

A software model of the olfactory system is presented as a test bed for ide ntifying and solving the problems of simulating the nonlinear dynamics of s ensory cortex. Compression, normalization and spatial contrast enhancement of the input to the bulb, the input stage of the olfactory system, are done by input-dependent attenuation of forward and lateral transmission, and by modulation of the asymptotic maximum of the sigmoid function of bulbar neu ral populations. An implementation of these mechanisms in the model, consti tuting local homeostatic regulation at the input stage, stabilizes the mode l in respect to variations in analog input and to recovery from repeated in put-induced state transitions. Both non-Hebbian habituation and Hebbian rei nforcement constituting local homeostatic regulation are used to train the model. A spatially patterned analog input belonging to a previously learned class may then guide the system to an appropriate basin of attraction. The se advances have improved the classification performance of the model but r eveal a still unsolved problem: the prestimulus state is governed by a glob al attractor, but the learned states are governed by collections of local a ttractors, not the desired global states.