Thesis on sensorimotor integration available

[Zoubin Ghahramani]

The following PhD thesis is available at 

	http://www.cs.utoronto.ca/~zoubin/   
or	ftp://ftp.cs.toronto.edu/pub/zoubin/thesis.ps.Z 

----------------------------------------------------------------------

      Computation and Psychophysics of Sensorimotor Integration

			  Zoubin Ghahramani
	       Department of Brain & Cognitive Sciences
		Massachusetts Institute of Technology


			       ABSTRACT

All higher organisms are able to integrate information from multiple
sensory modalities and use this information to select and guide
movements. In order to do this, the central nervous system (CNS) must
solve two problems: (1) Converting information from distinct sensory
representations into a common coordinate system, and (2) integrating
this information in a sensible way. This dissertation proposes a
computational framework, based on statistics and information theory,
to study these two problems. The framework suggests explicit models
for both the coordinate transformation and integration problems, which
are tested through human psychophysics.

The experiments in Chapter 2 suggest that: (1) Spatial information
from the visual and auditory systems is integrated so as to minimize
the variance in localization. (2) When the relation between visual and
auditory space is artificially remapped, the spatial pattern of
auditory adaptation can be predicted from its localization
variance. These studies suggest that multisensory integration and
intersensory adaptation are closely related through the principle of
minimizing localization variance. This principle is used to model
sensorimotor integration of proprioceptive and motor signals during
arm movements (Chapter 3). The temporal propagation of errors in
estimating the hand's state is captured by the model, providing
support for the existence of an internal model in the CNS that
simulates the dynamic behavior of the arm.

The coordinate transformation problem is examined in the visuomotor
system, which mediates reaching to visually-perceived objects (Chapter
4). The pattern of changes induced by a local remapping of this
transformation suggests a representation based on units with large
functional receptive fields. Finally, the problem of converting
information from disparate sensory representations into a common
coordinate system is addressed computationally (Chapter 5). An
unsupervised learning algorithm is proposed based on the principle of
maximizing mutual information between two topographic maps. What
results is an algorithm which develops multiple, mutually-aligned
topographic maps based purely on correlations between the inputs to
the different sensory modalities.


	(212 pages, 2.6 Mb, formatted for double-sided printing).