Self-organization of V1 Complex Cells Based On Slow Feature Analysis And Retinal Waves
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1
Humboldt Universität zu Berlin, Bernstein Center for Computational Neuroscience, Germany
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2
Humboldt Universität zu Berlin, Institute for Theoretical Biology, Germany
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3
Ruhr-Universität Bochum, Institut für Neuroinformati, Germany
The structure of the early visual system, most notably simple and complex cells in primary visual cortex(V1), is believed to be very well adapted to the statistical regularities present in its natural input (Field 1994).In fact, a number of theoretical studies have shown that some of these structural properties are optimal withrespect to certain coding objectives such as sparseness (Olshausen & Field 1996), information maximization(Bell & Sejnowski 1997), or slowness (Berkes & Wiskott 2005). These studies have also demonstrated how simple and complex cells can emerge in the process of optimizing such a coding objective by training onnatural images (or natural image sequences). However, some elements of the well-adapted structure of thevisual system are already present prior to the onset of vision and can thus not have been learned from naturalvisual input. Spontaneous neural activity, which spreads in waves across the retina, has been suggested toplay a major role in these prenatal structuring processes (Wong 1999).Here we present the results of applying a coding objective that optimizes for temporal slowness, namelySlow Feature Analysis (SFA) (Wiskott & Sejnowski 2002), to a biologically plausible model of retinal waves(Godfrey & Swindale 2007). After training with retinal wave image sequences, the resulting SFA units are subjected to sinusoidal test stimuli in order to characterize their response properties in a similar fashion as itis common practice in physiological experiments. We find that the SFA units reproduce a number of featuresreminiscent of cortical complex cells, including receptive fields with elongated and spatially segregated ONand OFF regions, several types of orientation tuning, frequency tuning, and very low F0/F1 values, which is indicative of a largely invariant response with respect to the phase (or position) of an input grating (figure 1).Further analysis of the SFA units reveals that the algorithm achieves the phase invariance by construction ofquadrature filter pairs, which is in line with classical models of complex cells.Our results support the idea that retinal waves share relevant spatial and temporal properties with naturalimages. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape thedeveloping early visual system so that it is best prepared for coding input from the natural world.
Figure 1: A Receptive fields of the first 25 SFA units. The units are ordered according to slowness, from leftto right and top to bottom. B Orientation tuning of the same SFA units as shown in A. Red (blue) linesindicate excitation (inhibition).C F1/F0 histogram. Neurons with F1/F0 values smaller than one areclassified as complex cells, neurons with values larger than one are classified as simple cells. The majority of our SFA units have F1/F0 values smaller than one and would thus be classified as complex cells.
Keywords:
computational neuroscience
Conference:
Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010.
Presentation Type:
Presentation
Topic:
Bernstein Conference on Computational Neuroscience
Citation:
Dähne
S,
Wilbert
N and
Wiskott
L
(2010). Self-organization of V1 Complex Cells Based On Slow Feature Analysis And Retinal Waves.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.fncom.2010.51.00090
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Received:
13 Sep 2010;
Published Online:
23 Sep 2010.
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Correspondence:
Dr. Sven Dähne, Humboldt Universität zu Berlin, Bernstein Center for Computational Neuroscience, Berlin, Germany, sven.daehne@gmail.com