BIROn - Birkbeck Institutional Research Online

    On simulating neural damage in Connectionist Networks

    Guest, O. and Caso, A. and Cooper, Richard P. (2020) On simulating neural damage in Connectionist Networks. Computational Brain & Behavior , ISSN 2522-087X. (In Press)

    [img] Text
    On_Simulating_Neural_Damage_in_Connectionist_Networks - singlespaced.pdf - Author's Accepted Manuscript
    Restricted to Repository staff only

    Download (790kB) | Request a copy

    Abstract

    A key strength of connectionist modelling is its ability to simulate both intact cognition and the behavioural effects of neural damage. We survey the literature, showing that models have been damaged in a variety of ways, e.g., by removing connections, by adding noise to connection weights, by scaling weights, by removing units, and by adding noise to unit activations. While these different implementations of damage have often been assumed to be behaviourally equivalent, some theorists have made aetiological claims that rest on nonequivalence. They suggest that related deficits with different aetiologies might be accounted for by different forms of damage within a single model. We present two case studies that explore the effects of different forms of damage in two influential connectionist models, each of which has been applied to explain neuropsychological deficits. Our results indicate that the effect of simulated damage can indeed be sensitive to the way in which damage is implemented, particularly when that environment comprises subsets of items that differ in their statistical properties, but such effects are sensitive to relatively subtle aspects of the model's training environment. We argue that, as a consequence, substantial methodological care is required if aetiological claims about simulated neural damage are to be justified, and conclude more generally that implementation assumptions, including those concerning simulated damage, must be fully explored when evaluating models of neurological deficits, both to avoid over-extending the explanatory power of specific implementations and to ensure that reported results are replicable.

    Metadata

    Item Type: Article
    Additional Information: The final publication is available at Springer via the link above.
    Keyword(s) / Subject(s): connectionism, connectionist neuropsychology, semantic cognition, sequential action selection, methodology, replication
    School: Birkbeck Schools and Departments > School of Science > Psychological Sciences
    Research Centres and Institutes: Cognition, Computation and Modelling, Centre for
    Depositing User: Rick Cooper
    Date Deposited: 03 Apr 2020 10:00
    Last Modified: 29 Jun 2020 09:46
    URI: http://eprints.bbk.ac.uk/id/eprint/31535

    Statistics

    Downloads
    Activity Overview
    2Downloads
    49Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item