QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

HOME  |   SEARCH  |   ARCHIVE  |   SUBSCRIBE  |   CONTACT  |   HELP

The Journal of Neuroscience, July 30, 2003, 23(17):6713-6727

This Article Full Text Full Text (PDF) Submit an eLetter Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (9) Citing Articles via Google Scholar Google Scholar Articles by Anastasio, T. J. Articles by Patton, P. E. Search for Related Content PubMed PubMed Citation Articles by Anastasio, T. J. Articles by Patton, P. E.

 Previous Article  |  Next Article 

A Two-Stage Unsupervised Learning Algorithm Reproduces Multisensory Enhancement in a Neural Network Model of the Corticotectal System

Thomas J. Anastasio^1,2 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 (Hebb-anti-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.

Key words: superior colliculus; multisensory integration; unsupervised learning; corticotectal system; neural network model; self-organization; Hebbian learning; anti-Hebbian learning

---

Received Nov. 27, 2002; revised Apr. 17, 2003; accepted Apr. 17, 2003.

This article has been cited by other articles:

				B. N. Carriere, D. W. Royal, and M. T. Wallace Spatial Heterogeneity of Cortical Receptive Fields and Its Impact on Multisensory Interactions J Neurophysiol, May 1, 2008; 99(5): 2357 - 2368. [Abstract] [Full Text] [PDF]

Home  |   Search  |   Archive  |   Subscribe  |   Contact  |   Help