Abstract
Biological and artificial sensory systems share many features and functionalities in common. One shared challenge is the management setup and maintenance of sensory topological information. In the case of a massive artificial sensory receptor array, this is an extremely complex problem. Biological sensory receptor arrays, such as the visual or tactile system, face the same problem and have found excellent solutions by implementing processes of sensory organization. Not only can biological sensory organization initiate the topological data construction, it can deal with growing systems and repair damaged ones. Importantly, it can use the patterned activity of sensory receptors to extract topological relationships. Using inspiration from these biological processes, we propose an activity-dependent clustering method for organizing large arrays of artificial sensory receptors. We present an algorithm that proceeds hierarchically by building a quadtree description of sensory organization and possesses many qualities of its biological counterpart, namely it can operate autonomously, it uses the patterned activity of sensory receptors and it is capable of supporting growth and repair.
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References
Coombes S, Laing C (2009) Delays in activity-based neural networks. Philos Trans R Soc A Math Phys Eng Sci 367:1117–1129. doi:10.1098/rsta2008.0256
Cottrell M, Fort JC (1986) A stochastic model of retinotopy: a self-organizing process. Biol Cybern 53:405–411
Ghosh A, Sydekum E, Haiss F, Peduzzi S, Zörner B, Schneider R, Baltes C, Rudin M, Weber B, Schwab ME (2009) Functional and anatomical reorganization of the sensory-motor cortex after incomplete spinal cord injury in adult rats. J Neurosci 29(39):12,210–12,219 http://www.ncbi.nlm.nih.gov/pubmed/19793979
Goodman CS, Shatz CJ (1993) Developmental mechanisms that generate precise patterns of neuronal connectivity. Cell 72(Suppl):77–98
Güßmann M, Pelster A, Wunner G (2007) Synergetic analysis of the Häussler-von der Malsburg equations for manifolds of arbitrary geometry. Annalen der Physik 519:379–394. doi:10.1002/andp.200610243
Häussler AF, von der Malsburg C (1983) Development of retinotopic projections—an analytical treatment. J Theor Neurobiol 2:47–73
Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York
Horisaki R, Kagawa K, Nakao Y, Tanida J (2010) Irregular lens arrangement design to improve imaging performance of compound-eye imaging systems. Appl Phys Express 3(2):022–501. doi:10.1143/APEX.3.022501
Hornsey R, Thomas P, Wong W, Pepic S, Yip K, Krishnasamy R (2004) Electronic compound-eye image sensor: construction and calibration. In: Blouke MM, Sampat N, Motta RJ (eds) Society of photo-optical instrumentation engineers (SPIE) conference series, society of photo-optical instrumentation engineers (SPIE) conference series, vol. 5301, pp 13–24 doi:10.1117/12.526811
Johnson-Frey SH (2004) Stimulation through simulation? motor imagery and functional reorganization in hemiplegic stroke patients. Brain and Cognition 55(2):328–331 http://www.ncbi.nlm.nih.gov/pubmed/15177807
Kaas JH (2000) The reorganization of sensory and motor maps after injury in adult mammals. In: Gazzaniga MS (ed) The new cognitive neurosciences, 2nd edn. The MIT Press, Cambridge, pp 223–236
Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480
Nassi JJ, Callaway EM (2009) Parallel processing strategies of the primate visual system. Nature reviews. Neuroscience 10(5):360–372. doi:10.1038/nrn2619
Nelles G, Spiekermann G, Jueptner M, Leonhardt G, Mueller S, Gerhard H, Diener HC (1999) Reorganization of sensory and motor systems in hemiplegic stroke patients. A positron emission tomography study. Stroke J Cereb Circul 30(8):1510–1516 http://www.ncbi.nlm.nih.gov/pubmed/10436092
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905. doi:10.1109/34.868688
Shih WP, Tsao LC, Lee CW, Cheng MY, Chang C, Yang YJ, Fan KC (2010) Flexible temperature sensor array based on a graphite-polydimethylsiloxane composite. Sensors 10(4):3597–3610. doi:10.3390/s100403597 http://www.mdpi.com/1424-8220/10/4/3597/
Wandell B, Dumoulin S, Brewer A (2007) Visual field maps in human cortex. Neuron 56:366–383
Wu Z, Leahy R (1993) An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans Pattern Anal Mach Intell 15(11):1101–1113. doi:10.1109/34.244673
Zhu J (2008) Synaptic formation rate as a control parameter in a model for the ontogenesis of retinotopy. In: Proceedings of the 18th international conference on Artificial Neural Networks, Part II, ICANN ’08, pp. 462–470. Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-87559-848
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Requena-Carrión, J., Wilby, M.R., Rodríguez-González, A.B. et al. An activity-dependent hierarchical clustering method for sensory organization. Biol Cybern 108, 49–60 (2014). https://doi.org/10.1007/s00422-013-0577-z
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DOI: https://doi.org/10.1007/s00422-013-0577-z