Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: An often-cited advantage of connectionist networks is that they 
        degrade gracefully under damage.
        Most demonstrations of the effects of damage and subsequent 
        relearning in these networks have only looked at very general 
        measures of performance.
        More recent studies suggest that damage in connectionist 
        networks can reproduce the specific patterns of behavior of 
        patients with neurological damage, supporting the claim that 
        these networks provide insight into the neural implementation of
        cognitive processes.
        However, the existing demonstrations are not very general, and
        there is little understanding of what underlying principles are 
        responsible for the results.
        This thesis investigates the effects of damage in connectionist 
        networks in order to analyze their behavior more thoroughly and 
        assess their effectiveness and generality in reproducing 
        neuropsychological phenomena.
        
        We focus on connectionist networks that make familiar patterns 
        of activity into stable "attractors."
        Unit interactions cause similar but unfamiliar patterns to move
        towards the nearest familiar pattern, providing a type of 
        "clean-up."
        In unstructured tasks, in which inputs and outputs are 
        arbitrarily related, the boundaries between attractors can help
        "pull apart" very similar inputs into very different final 
        patterns.
        Errors arise when damage causes the network to settle into a 
        neighboring but incorrect attractor.
        In this way, the pattern of errors produced by the damaged 
        network reflects the layout of the attractors that develop 
        through learning.
        
        In a series of simulations in the domain of reading via meaning,
        networks are trained to pronounce written words via a simplified
        representation of their semantics.
        This task is unstructured in the sense that there is no 
        intrinsic relationship between a work and its meaning.
        Under damage, the networks produce errors that show a 
        distribution of visual and semantic influences quite similar to
        that of brain-injured patients with "deep dyslexia."
        Further simulations replicate other characteristics of these 
        patients, including additional error types, better performance 
        on concrete vs. abstract words, preserved lexical decision, and
        greater confidence in visual vs. semantic errors.
        A range of network architectures and learning procedures produce
        qualitatively similar results, demonstrating that the layout of
        attractors depends more on the nature of the task than on the 
        architectural details of the network that enable the attractors
        to develop.
        
        Additional simulations address issues in relearning after damage
        : the speed of recovery, degree of generalization, and 
        strategies for optimizing recovery.
        Relative differences in the degree of relearning and 
        generalization for different network lesion locations can be 
        understood in terms of the amount of structure in the subtasks
        performed by parts of the network.
        
        Finally, in the related domain of object recognition, a similar
        network is trained to generate semantic representations of 
        objects from high-level visual representations.
        In addition to the standard weights, the network has 
        correlational weights useful for implementing short-term
        associative memory.
        Under damage, the network exhibits the complex semantic and 
        perseverative effects of patients with a visual naming disorder
        known as "optic aphasia," in which previously presented objects 
        influence the response to the current object.
        Like optic aphasics, the network produces predominantly semantic
        rather than visual errors because, in contrast to reading, there
        is some structure in the mapping from visual to semantic 
        representations for objects.
        
        Taken together, the results of the thesis demonstrate that the
        breakdown and recovery of behavior in lesioned attractor 
        networks reproduces specific neuropsychological phenomena by 
        virtue of the way the structure of a task shapes the layout of
        attractors.
        
        
Number: CMU-CS-91-185
Bibtype: TechReport
Month: sep
Author: David C. Plaut
Title: Connectionist Neuropsychology:
        The Breakdown and Recovery of Behavior in Lesioned 
        Attractor Networks
Year: 1991
Address: Pittsburgh, PA
Super: @CMUTR