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Neuron-Based Control Mechanisms for a Robotic Arm and Hand

Authors: Nishant Singh, Christian Huyck, Vaibhav Gandhi, Alexander Jones

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.

Keywords: Robot, neuron, cell assembly, spiking neuron, force sensitive resistor.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128871

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References:


[1] 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.
[2] C. Mead, “Neuromorphic electronic systems,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1629–1636, 1990.
[3] H. Markram, “The blue brain project,” Nature Reviews Neuroscience, vol. 7, no. 2, pp. 153–160, feb 2006.
[4] 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.
[5] 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.
[6] 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.
[7] E. Marder, “Motor pattern generation,” Current Opinion in Neurobiology, vol. 10, no. 6, pp. 691 – 698, 2000.
[8] 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.
[9] 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.
[10] C. R. Huyck and P. J. Passmore, “A review of cell assemblies,” Biological Cybernetics, vol. 107, no. 3, pp. 263–288, apr 2013.
[11] 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.
[12] J. Feng, “Is the integrate-and-fire model good enough? a review,” Neural Networks, vol. 14, no. 67, pp. 955 – 975, 2001.
[13] M. Gewaltig and M. Diesmann, “Nest (neural simulation tool),” Scholarpedia, vol. 2, no. 4, p. 1430, 2007.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] E. Byrne and C. Huyck, “Processing with cell assemblies,” Neurocomputing, vol. 74, no. 13, pp. 76 – 83, 2010.
[25] (Online). Available: www.cwa.mdx.ac.uk/NEAL/code/simpRobot.html Accessed on 17/01/2017.
[26] P. H. Goodman, “Framework and implications of virtual neurorobotics,” Frontiers in Neuroscience, vol. 2, no. 1, pp. 123–128, jul 2008.
[27] J. R. Anderson and C. Lebiere, “The atomic components of thought,” 1998.
[28] J. E. Laird, A. Newell, and P. S. Rosenbloom, “Soar: An architecture for general intelligence,” 1987.
[29] 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.
[30] 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.