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Neural network modelling of the influence of channelopathies on reflex visual attention

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Abstract

This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network’s rate of failure to shift attention is lower than the control network’s, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children.

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Notes

  1. The model in (O’Reilly and Munakata 2000) uses a one-dimensional input space comprising of 7 discrete locations, 2 input categories input on a 14-units map diverging into a dorsal and a ventral pathway, each comprising of one hidden and one output layer.

  2. See authors’ note at grey.colourado.edu/CompCogNeuro.

  3. Autistic subjects often suffer from a disturbed immune system (Ashwood et al. 2006). In parallel, the importance of calcium homeostasis in the immune response is evidenced by the cytopathic effects of the Ca2+ homeostatic imbalance triggered by several viral infections, see for instance (Poggi et al. 1998; Zocchi et al. 1998; Cheshenko et al. 2003). Misra et al. (1999) also showed that beryllium toxicity is in part the result of altered Ca2+ metabolism in mononuclear phagocytes consequent to reversible opening of plasma membrane channels, which not only reveals the central role of calcium homeostasis in the immune system, but also that of membrane calcium channels in that process.

  4. These changes in timings of accommodation and hysteresis are simulated by decreasing respectively \(dt_{b_a,inc}\) and \(dt_{b_h,inc}\), as explained in Eqs. 4 and 5.

  5. Event-related potentials are EEG-recordable correlates of motor or cognitive events.

  6. Throughout this paper, reflex visual attention is also called bottom-up visual attention, visual attention capture, or, where there is no possible ambiguity, visual attention.

  7. The term “attentional spotlight” is mostly used to illustrate the selectivity of attention. It may not be a good illustration of the neurological processes underlying attention.

  8. Cortical map is the term used to name a sheet of cortical neurons with similar functions.

  9. This decussation of the optic nerves does not affect the information processing in EVAC.

  10. In equations, literal symbols that relate to a particular channel are subscripted by a letter that identifies the channel, or by \(\alpha\) for a generic expression applicable to several channels.

  11. The model’s V1 minicolumns can be minimised into 8 units, covering 4 segments orientations and 2 polarities. In that case, LGN-to-V1 projection weights are not learnt but pre-defined. This is the case in the final implementation of EVAC.

  12. Emergent version 5.0.2, 32 bit, available freely on Internet at grey.colourado.edu

  13. See authors’ notes on their website at grey.colourado.edu/CompCogNeuro.

  14. The 60 % threshold is arbitrary, but follows the convention of O’Reilly and Munakata (2000). Moreover, in the model of EVAC, the spurious activation of two or more semantic output units by more than 60 % each is made impossible by the use of a strict 1-WTA inhibition rule over the semantic output layer.

  15. The classification error rate \(r_\epsilon\) is \(r_\epsilon ={N_\epsilon }/{N}\), where \(N_\epsilon\) is the number trials where the wrong semantic output unit is activated by more than 0.6 in less than 301 time units. The activation failure rate \(r_\zeta\) is defined by \(r_\zeta ={N_\zeta }/{N}\) where \(N_\zeta\) is the number of trials where no semantic output unit gets activated by more than 0.6 in less than 301 time units.

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Acknowledgments

We thank the reviewers for their comments made on an earlier version of the manuscript. This paper is based on the doctoral dissertation work of Alexandre Gravier, which was funded by Nanyang Technological University.

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Authors and Affiliations

  1. Centre for Computational Intelligence (C2i), Nanyang Technological University, N4-B1A-02, Nanyang Avenue, 639798, Singapore, Singapore

    Alexandre Gravier

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Chai Quek

  3. Department of Informatics, School of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Torun, Poland

    Włodzisław Duch

  4. School of Information and Communication Technology, International Islamic University of Malaysia, Kuala Lumpur, Malaysia

    Abdul Wahab

  5. School of Humanities and Social Sciences, Nanyang Technological University, Singapore, Singapore

    Joanna Gravier-Rymaszewska

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  1. Alexandre Gravier
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Correspondence to Alexandre Gravier.

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Gravier, A., Quek, C., Duch, W. et al. Neural network modelling of the influence of channelopathies on reflex visual attention. Cogn Neurodyn 10, 49–72 (2016). https://doi.org/10.1007/s11571-015-9365-x

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