Abstract
We have developed a new simulation environment, called NeuVision, that is able to perform neuro-robotic experiments in a closed-loop architecture, by simulating a large-scale neuronal network bi-directionally connected to a robot. We conceived it primarily as a support tool to be used in the context of the ‘embodied electrophysiology’, a growing field that could help, in the future, to realize innovative bi-directional and adaptive Brain-Machine Interfaces. The main features of our system are related to the efficient visualization of the neural activity, the possibility to define different connectivity rules and stimulation points, and the integration of statistical analysis tools for fast neural dynamics characterization. Our preliminary results show that we are able to reproduce both spontaneous and evoked activity of cultured networks. Hence, by defining a decoding strategy based on the Center of Activity, we carried out experiments with a simulated neuronal network in a closed-loop with a robot. Our results suggest the NeuVision simulated environment could be used as a tool to support in vitro experiments with real systems.
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R.D. Beer, H.J. Chiel, R.D. Quinn, and R.E. Ritzmann, Biorobotic approaches to the study of motor systems, Current Opinion in Neurobiology, 8 (1998), 777–782
W.R. Ashby, Design for a brain, Chapman and Hall, London, 1952
T.B. DeMarse, D.A. Wagenaar, A.W. Blau, and S.M. Potter, The neurally controlled animat: biological brains acting with simulated bodies, Autonomous Robots, 11 (2001), 305–310
D.J. Bakkum, A.C. Shkolnik, G. Ben-Ary, P. Gamblen, T.B. De- Marse, and S.M. Potter, Removing some ‘A’ from AI: Embodied cultured networks, Embodied Artificial Intelligence, 3139, 2004, 130–145.
G. Le Masson, S. Renaud-Le Masson, D. Debay, and T. Bal, Feedback inhibition controls spike transfer in hybrid thalamic circuits, Nature, 417, 2002, 854–8.
D.A. Wagenaar, R. Madhavan, J. Pine, and S.M. Potter, Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation, J. Neurosci., 25, 2005, 680–8.
G. Shahaf and S. Marom, Learning in networks of cortical neurons, J. Neurosci., 21, 2001, 8782–8.
S. Marom and D. Eytan, Learning in ex-vivo developing networks of cortical neurons, Prog. Brain. Res., 147, 2005, 189–99.
B.D. Reger, K.M. Fleming, V. Sanguineti, S. Alford, and F.A. Mussa-Ivaldi, Connecting brains to robots: an artificial body for studying the computational properties of neural tissues, Artif Life, 6, 2000, 307–24.
A. Karniel, M. Kositsky, K.M. Fleming, M. Chiappalone, V. Sanguineti, S. Alford, and F.A. Mussa-Ivaldi, Computational analysis in vitro: recurrent dynamics and plasticity in a neuro-robotic system, Journal of Neural Engineering, 2, 2005, S250–S265.
S. Martinoia, V. Sanguineti, L. Cozzi, L. Berdondini, J. Van Pelt, J. Tomas, G. Le Masson, and F.A. Davide, Towards an embodied in-vitro electrophysiology: the NeuroBIT project, Neurocomputing, 58–60, 2004, 1065–1072.
A. Novellino, P. D’Angelo, L. Cozzi, M. Chiappalone, V. Sanguineti, and S. Martinoia, Connecting neurons to a mobile robot: an in vitro bidirectional neural interface, Computational Intelligence and Neuroscience, 2007.
S.M. Potter, Closing the loop between neurons and neurotechnology, Frontiers in Neuroscience, 4, 2010, 15.
M. Mulas, S. Martinoia, and P. Massobrio, NeuVision: a novel simulation environment to model spontaneous and stimulus-evoked activity of large-scale neuronal networks, Submitted to Neurocomputing.
J. Van Pelt, M.A. Corner, P.S. Wolters, W.L. Rutten, and G.J. Ramakers, Longterm stability and developmental changes in spontaneous network burst firing patterns in dissociated rat cerebral cortex cell cultures on multielectrode arrays, Neuroscience Letters, 361, 2004, 86–89.
Z.C. Chao, D.J. Bakkum, and S.M. Potter, Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics, Journal of Neural Engineering, 4, 2007, 294–308.
E.M. Izhikevich, Simple model of spiking neurons, IEEE Transactions on Neural Networks, 6, 2003, 1569–1572
E.M. Izhikevich, Which model to use for cortical spiking neurons?, IEEE Transactions on Neural Networks, 15, 2004, 1063–1070.
V. Braitenberg and A. Schultz, Anatomy of the cortex: statistics and geometry, Springer-Verlag, Berlin, 1991.
S. Marom and G. Shahaf, Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy, Quarterly Reviews of Biophysics, 35, 2002, 63–87.
B.J. Dworak and B.C. Wheeler, Novel MEA platform with PDMS microtunnels enables the detection of action potential propagation from isolated axons in culture, Lab on a Chip, 9, 2009, 404–410.
B.M. Waxman, Routing of multipoint connections, IEEE Journal on Selected Areas in Communications, 6, 1988, 1617–1622.
M. Chiappalone, M. Bove, A. Vato, M. Tedesco, and S. Martinoia, Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development, Brain Research, 1093, 2006, 41–53.
V. Pasquale, P. Massobrio, L.L. Bologna, M. Chiappalone, and S. Martinoia, Self-organization and neuronal avalanches in networks of dissociated cortical neurons, Neuroscience, 153, 2008, 1354–1369.
L.L. Bologna, T. Nieus, M. Tedesco, M. Chiappalone, F. Benfenati, and S. Martinoia, Low-frequency stimulation enhances burst activity in cortical cultures during development, Neuroscience, 165, 2010, 692–704.
M. Chiappalone, P. Massobrio, and S. Martinoia, Network plasticity in cultured cortical assemblies, European Journal of Neuroscience, 28, 2008, 221–237.
Z.C. Chao, D.J. Bakkum, and S.M. Potter, Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics, Journal of Neural Engineering, 4, 2007, 294–308.
M. Gandolfo, A. Maccione, M. Tedesco, S. Martinoia, and L. Berdondini, Tracking burst patterns in hippocampal cultures with high-density CMOS-MEAs, Journal of Neural Engineering, 7, 2010, 056001.
Z.C. Chao, D.J. Bakkum, and S.M. Potter, Shaping Embodied Neural Networks for Adaptive Goal-directed Behavior, PLoS Computational Biology, 4, 2008, e1000042.
N.T. Carnevale and M.L. Hines, The NEURON Book, Cambridge University Press, 2005.
J.M. Bower and D. Beeman, The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System, Springer-Verlag, New York, 1998.
M.-O. Gewaltig and M. Diesmann, NEST (neural simulation tool), in Scholarpedia, 2007, p. 1430.
D. Goodman and R. Brette, Brian: a simulator for spiking neuronal networks in Python, Frontiers in Neuroinformatics, 2, 2008.
D. Pecevski, T. Natschlager, and K. Schuch, PCSIM: a parallel simulation environment for neural circuits fully integrated with Python, Frontiers in Neuroinformatics, 3, 2009.
B. Aisa, B. Mingus, and R. O’Reilly, The emergent neural modeling system, Neural Networks, 21, 2008, 1045–1212.
C. Balkenius, J. Morén, B. Johansson, and M. Johnsson, Ikaros: building cognitive models for robots, Advanced Engineering Informatics, 24 2010, 40–48.
S. Carpin, M. Lewis, J. Wang, S. Balakirsky, and C. Scrapper, USARSim: a robot simulator for research and education, Proceedings of IEEE International Conference on Robotics and Automation, 2007, 1400–1405.
B. Gerkey, R.T. Vaughan, and A. Howard, The Player/Stage project: tools for multi-robot and distributed sensor systems, Proceedings of Proceedings of the 11th International Conference on Advanced Robotics (ICAR 2003) 2003, 317–323.
Microsoft. Microsoft Robotics Developer Studio. 2008 [cited]; Available from: http://www.microsoft.com/robotics/.
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Mulas, M., Massobrio, P., Martinoia, S. et al. A simulated neuro-robotic environment for bi-directional closed-loop experiments. Paladyn 1, 179–186 (2010). https://doi.org/10.2478/s13230-011-0004-x
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DOI: https://doi.org/10.2478/s13230-011-0004-x