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PSPs and ERPs: Applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended Event-Related Potential reading data.

The Parallel Distributed Processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a P...

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Published in:Brain & Language Vol. 132; pp. 22 - 28
Main Authors: Laszlo, Sarah, Armstrong, Blair C
Format: Journal Article
Published: Academic Press Inc. May2014
Online Access:View this record in EBSCOhost
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      dt: May2014
      vid: 132
      pid: 735
      pub: Academic Press Inc.
      place: Burlington, Massachusetts
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        103822899
        2012579374
        10.1016/j.bandl.2014.03.002
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        atl: PSPs and ERPs: Applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended Event-Related Potential reading data.
      aug:
        au:
          Laszlo, Sarah
          Armstrong, Blair C
        affil: Department of Psychology, State University of New York, Binghamton, 4400 Vestal Parkway East, Binghamton, NY 13902, United States. Electronic address: cogneuro@alum.mit.edu.
      sug:
        subj:
          Cognition
          Evoked Potentials Physiology
          Models, Biological
          Reading
          Membrane Potentials Physiology
          Language
          Neural Networks (Computer)
      ab: The Parallel Distributed Processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a PDP model of reading-related components in the Event-Related Potential (ERP) to simulation of the N400 repetition effect. We accomplish this by incorporating the dynamics of cortical post-synaptic potentials-the source of the ERP signal-into the model. Simulations demonstrate that application of these dynamics is critical for model elicitation of repetition effects in the time and frequency domains. We conclude that by advancing a neurocomputational understanding of repetition effects, we are able to posit an interpretation of their source that is both explicitly specified and mechanistically different from the well-accepted cognitive one.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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