# Show Reference: "A Two-Stage Unsupervised Learning Algorithm Reproduces Multisensory Enhancement in a Neural Network Model of the Corticotectal System"

A Two-Stage Unsupervised Learning Algorithm Reproduces Multisensory Enhancement in a Neural Network Model of the Corticotectal System The Journal of Neuroscience, Vol. 23, No. 17. (30 July 2003), pp. 6713-6727 by Thomas J. Anastasio, Paul E. Patton
@article{anastasio-and-patton-2003,
abstract = {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.},
author = {Anastasio, Thomas J. and Patton, Paul E.},
day = {30},
issn = {1529-2401},
journal = {The Journal of Neuroscience},
keywords = {learning, multi-modality, sc, som, unsupervised-learning, visual},
month = jul,
number = {17},
pages = {6713--6727},
pmid = {12890764},
posted-at = {2011-12-14 11:37:59},
priority = {2},
publisher = {Society for Neuroscience},
title = {A {Two-Stage} Unsupervised Learning Algorithm Reproduces Multisensory Enhancement in a Neural Network Model of the Corticotectal System},
url = {http://www.jneurosci.org/content/23/17/6713.abstract},
volume = {23},
year = {2003}
}


Anastasio and Patton model the deep SC using SOM learning.

Anastasio and Patton present a model of multi-sensory integration in the superior colliculus which takes into account modulation by uni-sensory projections from cortical areas.

In the model due to Anastasio and Patton, deep SC neurons combine cortical input multiplicatively with primary input.

Anastasio and Patton's model is trained in two steps:

First, connections from primary input to deep SC neurons are adapted in a SOM-like fashion.

Then, connections from uni-sensory, parietal inputs are trained, following an anti-Hebbian regime.

The latter phase ensures the principles of modality-matching and cross-modality.

Modulatory input from uni-sensory, parietal regions to SC follows the principles of modality-matching and cross-modality:

A deep SC neuron (generally) only receives modulatory input related to some modality if it also receives primary input from that modality.

Modulatory input related to some modality only affects responses to primary input from the other modalities.

SOM learning produces clusters of neurons with similar modality responsiveness in the SC model due to Anastasio and Patton.

The model due to Anastasio and Patton reproduces multi-sensory enhancement.

Deactivating modulatory, cortical input also deactivates multi-sensory enhancement.

In Anastasio and Patton's SC model, the spatial organization of the SOM is not used to represent the spatial organization of the outside world, but to distribute different sensitivities to the input modalities in different neurons.

It's a bit strange that Anastasio and Patton's and Martin et al.'s SC models do not use the spatial organization of the SOM to represent the spatial organization of the outside world, but to distribute different sensitivities to the input modalities in different neurons.

KNN (or sparse coding) seems to be more appropriate for that.

Anastasio drop the strong probabilistic interpretation of SC neurons' firing patterns in their learning model.

Depression in the SC happens between stimuli from the same modality.