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A computational neural network model of perisaccadic mislocalization in total darkness

Normally, visual space is perceived as stable across eye movements, but under certain conditions it becomes illusory. We are proposing a computational neural network model to explain a misperception called "perisaccadic shift" which occurs typically in complete darkness. In early visual areas, space is represented in retinotopic coordinates. Models for this misperception have to explain how stimulus position information is transformed into head-centered coordinates [1]. We are improving on existing models by integrating behavioral as well as numerous neurophysiological constraints and explain new empirical data [2].

Classically, the subtraction theory assumes a continuous extraretinal eye position signal (EEPS), which, if it is tuned perfectly to the eye movements, compensates any saccade-induced movements of stimuli on the retina. The perisaccadic shift illusion suggests that the stipulated EEPS does not follow the eye movements perfectly. Experimental findings indicate that the time course of the EEPS is much smoother than the actual eye movements [1, 3]. A major problem of the continuous EEPS is that there has been no neurophysiological evidence for it so far.

Our model uses a much more realistic "binary"eye position signal which does not represent the continuous movement of the eye but only its start and end positions (thus it is binary) [4]. When the eye is at rest, there is an eye position signal representing its position. At a time t1 before or during a saccade this signal goes off while at another time t2 the signal for the eye position after the saccade goes on. This eye position signal is accompanied by a phasic corollary discharge signal that encodes the saccade target. Coordinate transformation is achieved by a basis function network [5] that is embedded in a fully dynamic model. We followed the experimental procedure in [2] and tuned the timing parameters of the binary eye position signal to fit the experimental data.

We show that our model is able to reproduce for the first time all experimental findings, while it is based on much more realistic assumptions than previous models. In particular, we show that a) a continuous EEPS is not necessary, b) both an EEPS and a corollary discharge signal are necessary and c) a basis function network can well explain the experimental data.


  1. Pola J: Models of the mechanism underlying perceived location of a perisaccadic flash. Vision Research. 2004, 44: 2799-2813. 10.1016/j.visres.2004.06.008.

    Article  PubMed  Google Scholar 

  2. van Wetter SMCI, van Opstal AJ: Experimental test of visuomotor updating models that explain perisaccadic mislocalization. J Vision. 2008, 8: 1-22. 10.1167/8.14.8.

    Article  Google Scholar 

  3. Honda H: The time courses of visual mislocalization and of extraretinal eye position signals at the time of vertical saccades. Vision Res. 1991, 31: 1915-1921. 10.1016/0042-6989(91)90186-9.

    Article  CAS  PubMed  Google Scholar 

  4. Wang X, Zhang M, Cohen IS, Goldberg ME: The proprioceptive representation of eye position in monkey primary somatosensory cortex. Nature Neuroscience. 2007, 10: 640-646. 10.1038/nn1878.

    Article  CAS  PubMed  Google Scholar 

  5. Pouget A, Deneve S, Duhamel J: A computational perspective on the neural basis of multisensory spatial representations. Nature Reviews Neuroscience. 2002, 3: 741-747. 10.1038/nrn914.

    Article  CAS  PubMed  Google Scholar 

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This work has been supported by the Federal Ministry of Education and Research grant (BMBF 01GW0653).

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Correspondence to Arnold Ziesche.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Ziesche, A., Hamkerr, F.H. A computational neural network model of perisaccadic mislocalization in total darkness. BMC Neurosci 10 (Suppl 1), P363 (2009).

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