Commenced in January 2007
Paper Count: 30121
Neuron-Based Control Mechanisms for a Robotic Arm and Hand
Abstract:A robotic arm and hand controlled by simulated neurons is presented. The robot makes use of a biological neuron simulator using a point neural model. The neurons and synapses are organised to create a finite state automaton including neural inputs from sensors, and outputs to effectors. The robot performs a simple pick-and-place task. This work is a proof of concept study for a longer term approach. It is hoped that further work will lead to more effective and flexible robots. As another benefit, it is hoped that further work will also lead to a better understanding of human and other animal neural processing, particularly for physical motion. This is a multidisciplinary approach combining cognitive neuroscience, robotics, and psychology.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128871Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1263
 R. Brette and W. Gerstner, “Adaptive exponential integrate-and-fire model as an effective description of neuronal activity,” J. Neurophysiol., vol. 94, pp. 3637–3642, 2005.
 C. Mead, “Neuromorphic electronic systems,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1629–1636, 1990.
 H. Markram, “The blue brain project,” Nature Reviews Neuroscience, vol. 7, no. 2, pp. 153–160, feb 2006.
 D. S. Jeong, I. Kim, M. Ziegler, and H. Kohlstedt, “Towards artificial neurons and synapses: a materials point of view,” RSC Advances, vol. 3, no. 10, p. 3169, 2013.
 P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo, S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk, R. Manohar, and D. S. Modha, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science, vol. 345, no. 6197, pp. 668–673, aug 2014.
 N. Qiao, H. Mostafa, F. Corradi, M. Osswald, F. Stefanini, D. Sumislawska, and G. Indiveri, “A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses,” Frontiers in Neuroscience, vol. 9, apr 2015.
 E. Marder, “Motor pattern generation,” Current Opinion in Neurobiology, vol. 10, no. 6, pp. 691 – 698, 2000.
 L. Martignon, G. Deco, K. Laskey, M. Diamond, W. Freiwald, and E. Vaadia, “Neural coding: Higher-order temporal patterns in the neurostatistics of cell assemblies,” Neural Computation, vol. 12, no. 11, pp. 2621–2653, nov 2000.
 D. Angulo-Garcia, J. D. Berke, and A. Torcini, “Cell assembly dynamics of sparsely-connected inhibitory networks: A simple model for the collective activity of striatal projection neurons,” PLOS Computational Biology, vol. 12, no. 2, p. e1004778, feb 2016.
 C. R. Huyck and P. J. Passmore, “A review of cell assemblies,” Biological Cybernetics, vol. 107, no. 3, pp. 263–288, apr 2013.
 W. M. Kistler, W. Gerstner, and J. L. van Hemmen, “Reduction of the hodgkin-huxley equations to a single-variable threshold model,” Neural Computation, vol. 9, no. 5, pp. 1015–1045, jul 1997.
 J. Feng, “Is the integrate-and-fire model good enough? a review,” Neural Networks, vol. 14, no. 67, pp. 955 – 975, 2001.
 M. Gewaltig and M. Diesmann, “Nest (neural simulation tool),” Scholarpedia, vol. 2, no. 4, p. 1430, 2007.
 A. Hanuschkin, S. Kunkel, M. Helias, A. Morrison, and M. Diesmann, “A general and efficient method for incorporating precise spike times in globally time-driven simulations,” Frontiers in Neuroinformatics, vol. 4, 2010.
 S. Henker, J. Partzsch, and R. Schffny, “Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks,” Journal of Computational Neuroscience, vol. 32, no. 2, pp. 309–326, aug 2011.
 C. Huyck, C. Evans, and I. Mitchell, “A comparison of simple agents implemented in simulated neurons,” Biologically Inspired Cognitive Architectures, vol. 12, pp. 9 – 19, 2015.
 V. Gandhi, G. Prasad, D. Coyle, L. Behera, and T. M. McGinnity, “EEG-based mobile robot control through an adaptive brain –robot interface,” and Cybernetics: Systems IEEE Transactions on Systems, Man, vol. 44, no. 9, pp. 1278–1285, Sep. 2014.
 P. Haggard and M. Eimer, “On the relation between brain potentials and the awareness of voluntary movements,” Experimental brain research, vol. 126, no. 1, pp. 128–133, 1999.
 A. Schurger, J. D. Sitt, and S. Dehaene, “An accumulator model for spontaneous neural activity prior to self-initiated movement,” Proceedings of the National Academy of Sciences, vol. 109, no. 42, pp. E2904–E2913, aug 2012.
 L. P. J. Selen, M. N. Shadlen, and D. M. Wolpert, “Deliberation in the motor system: Reflex gains track evolving evidence leading to a decision,” Journal of Neuroscience, vol. 32, no. 7, pp. 2276–2286, feb 2012.
 R. Chen, Z. Yaseen, L. G. Cohen, and M. Hallett, “Time course of corticospinal excitability in reaction time and self-paced movements,” Annals of Neurology, vol. 44, no. 3, pp. 317–325, sep 1998.
 A. Jones and B. Forster, “Neural correlates of endogenous attention, exogenous attention and inhibition of return in touch,” European Journal of Neuroscience, vol. 40, no. 2, pp. 2389–2398, apr 2014.
 A. Davison, D. Br¨uderle, J. Eppler, E. Muller, D. Pecevski, L. Perrinet, and P. Yqer, “PyNN: a common interface for neuronal network simulators,” Frontiers in neuroinformatics, vol. 2, 2008.
 E. Byrne and C. Huyck, “Processing with cell assemblies,” Neurocomputing, vol. 74, no. 13, pp. 76 – 83, 2010.
 (Online). Available: www.cwa.mdx.ac.uk/NEAL/code/simpRobot.html Accessed on 17/01/2017.
 P. H. Goodman, “Framework and implications of virtual neurorobotics,” Frontiers in Neuroscience, vol. 2, no. 1, pp. 123–128, jul 2008.
 J. R. Anderson and C. Lebiere, “The atomic components of thought,” 1998.
 J. E. Laird, A. Newell, and P. S. Rosenbloom, “Soar: An architecture for general intelligence,” 1987.
 J. Jilk, C. Lebiere, R. O’Reilly, and J. Anderson, “Sal: An explicitly pluralistic cognitive architecture,” Journal of Experimental and Theoretical Artificial Intelligence, vol. 20(3), pp. 197–218, 2008.
 C. Eliasmith, T. Stewart, X. Choo, T. Bekolay, T. DeWolf, Y. Tang, and D. Rasmussen, “A large-scale model of the functioning brain,” Science, vol. 338(6111), pp. 1202–1205, 2012.