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Behavioral/Systems/Cognitive A Two-Stage Unsupervised Learning Algorithm Reproduces

Summary: Behavioral/Systems/Cognitive
A Two-Stage Unsupervised Learning Algorithm Reproduces
Multisensory Enhancement in a Neural Network Model of
the Corticotectal System
Thomas J. Anastasio1,2 and Paul E. Patton2
1Department of Molecular and Integrative Physiology and 2Beckman Institute, University of Illinois at Urbana/Champaign, Urbana, Illinois 61801
Multisensory enhancement (MSE) is the augmentation of the response to sensory stimulation of one modality by stimulation of a
different modality. It has been described for multisensory neurons in the deep superior colliculus (DSC) of mammals, which function to
detect, and direct orienting movements toward, the sources of stimulation (targets). MSE would seem to improve the ability of DSC
neurons to detect targets, but many mammalian DSC neurons are unimodal. MSE requires descending input to DSC from certain regions
of parietal cortex. Paradoxically, the descending projections necessary for MSE originate from unimodal cortical neurons. MSE, and the
puzzling findings associated with it, can be simulated using a model of the corticotectal system. In the model, a network of DSC units
receives primary sensory input that can be augmented by modulatory cortical input. Connection weights from primary and modulatory
inputs are trained in stages one (Hebb) and two (Hebb­anti-Hebb), respectively, of an unsupervised two-stage algorithm. Two-stage
training causes DSC units to extract information concerning simulated targets from their inputs. It also causes the DSC to develop a
mixture of unimodal and multisensory units. The percentage of DSC multisensory units is determined by the proportion of cross-modal
targets and by primary input ambiguity. Multisensory DSC units develop MSE, which depends on unimodal modulatory connections.
Removal of the modulatory influence greatly reduces MSE but has little effect on DSC unit responses to stimuli of a single modality. The
correspondence between model and data suggests that two-stage training captures important features of self-organization in the real
corticotectal system.


Source: Anastasio, Thomas J. - Beckman Institute for Advanced Science and Technology & Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign


Collections: Biology and Medicine