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A neuroanatomically grounded Hebbian-learning model of attention–language interactions in the human brain

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      Abstract

      Meaningful familiar stimuli and senseless unknown materials lead to different patterns of brain activation. A late major neurophysiological response indexing ‘sense’ is the negative component of event-related potential peaking at around 400 ms (N400), an event-related potential that emerges in attention-demanding tasks and is larger for senseless materials (e.g. meaningless pseudowords) than for matched meaningful stimuli (words). However, the mismatch negativity (latency 100–250 ms), an early automatic brain response elicited under distraction, is larger to words than to pseudowords, thus exhibiting the opposite pattern to that seen for the N400. So far, no theoretical account has been able to reconcile and explain these findings by means of a single, mechanistic neural model. We implemented a neuroanatomically grounded neural network model of the left perisylvian language cortex and simulated: (i) brain processes of early language acquisition and (ii) cortical responses to familiar word and senseless pseudoword stimuli. We found that variation of the area-specific inhibition (the model correlate of attention) modulated the simulated brain response to words and pseudowords, producing either an N400- or a mismatch negativity-like response depending on the amount of inhibition (i.e. available attentional resources). Our model: (i) provides a unifying explanatory account, at cortical level, of experimental observations that, so far, had not been given a coherent interpretation within a single framework; (ii) demonstrates the viability of purely Hebbian, associative learning in a multilayered neural network architecture; and (iii) makes clear predictions on the effects of attention on latency and magnitude of event-related potentials to lexical items. Such predictions have been confirmed by recent experimental evidence.

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      Neural networks and physical systems with emergent collective computational abilities.

       John Hopfield (1982)
      Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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        Neural mechanisms of selective visual attention.

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          LTP and LTD: an embarrassment of riches.

          LTP and LTD, the long-term potentiation and depression of excitatory synaptic transmission, are widespread phenomena expressed at possibly every excitatory synapse in the mammalian brain. It is now clear that "LTP" and "LTD" are not unitary phenomena. Their mechanisms vary depending on the synapses and circuits in which they operate. Here we review those forms of LTP and LTD for which mechanisms have been most firmly established. Examples are provided that show how these mechanisms can contribute to experience-dependent modifications of brain function.
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            Author and article information

            Affiliations
            [1 ]simpleMRC Cognition & Brain Sciences Unit 15 Chaucer Road, Cambridge CB2 7EF, UK
            [2 ]simpleCentre for Theoretical and Computational Neuroscience, University of Plymouth Plymouth, UK
            Author notes
            Correspondence: Dr Max Garagnani, as above. E-mail: Max.Garagnani@ 123456mrc-cbu.cam.ac.uk

            Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

            Journal
            Eur J Neurosci
            ejn
            The European Journal of Neuroscience
            Blackwell Publishing Ltd
            0953-816X
            1460-9568
            January 2008
            : 27
            : 2
            : 492-513
            2258460
            18215243
            10.1111/j.1460-9568.2008.06015.x
            © The Authors (2008). Journal Compilation © Federation of European Neuroscience Societies and Blackwell Publishing Ltd
            Categories
            Research Reports

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