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Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications

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In Vitro Neuronal Networks

Part of the book series: Advances in Neurobiology ((NEUROBIOL,volume 22))

Abstract

One of the main limitations preventing the realization of a successful dialogue between the brain and a putative enabling device is the intricacy of brain signals. In this perspective, closed-loop in vitro systems can be used to investigate the interactions between a network of neurons and an external system, such as an interacting environment or an artificial device. In this chapter, we provide an overview of closed-loop in vitro systems, which have been developed for investigating potential neuroprosthetic applications. In particular, we first explore how to modify or set a target dynamical behavior in a network of neurons. We then analyze the behavior of in vitro systems connected to artificial devices, such as robots. Finally, we provide an overview of biological neuronal networks interacting with artificial neuronal networks, a configuration currently offering a promising solution for clinical applications.

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References

  • Abdulkader, S. N., Atia, A., & Mostafa, M. S. M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16, 213–230.

    Article  Google Scholar 

  • Adrian, E. D. (1928). The basis of sensation. New York: W W Norton & Co.

    Google Scholar 

  • Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., et al. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. Lancet, 389, 1821–1830.

    Article  PubMed  PubMed Central  Google Scholar 

  • Arlotti, M., Marceglia, S., Foffani, G., Volkmann, J., Lozano, A. M., Moro, E., et al. (2018). Eight-hours adaptive deep brain stimulation in patients with Parkinson disease. Neurology, 90, e971–e976.

    Article  PubMed  PubMed Central  Google Scholar 

  • Armstrong, C., Krook-Magnuson, E., Oijala, M., & Soltesz, I. (2013). Closed-loop optogenetic intervention in mice. Nature Protocols, 8, 1475.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bakkum, D. J., Gamblen, P. M., Ben-Ary, G., Chao, Z. C., & Potter, S. M. (2007). MEART: The semi-living artist. Frontiers in Neurorobotics, 1, 5.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bamford, J. A., Putman, C. T., & Mushahwar, V. K. (2005). Intraspinal microstimulation preferentially recruits fatigue-resistant muscle fibres and generates gradual force in rat. The Journal of Physiology, 569, 873–884.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bareket-Keren, L., & Hanein, Y. (2012). Carbon nanotube-based multi electrode arrays for neuronal interfacing: Progress and prospects. Front Neural Circuits, 6, 122.

    PubMed  Google Scholar 

  • Ben-Ari, Y. (2001). Developing networks play a similar melody. Trends in Neurosciences, 24, 353–360.

    Article  CAS  PubMed  Google Scholar 

  • Berdondini, L., Chiappalone, M., Van Der Wal, P., Imfeld, K., de Rooij, N. F., Koudelka-Hep, M., et al. (2006). A microelectrode array (MEA) integrated with clustering structures for investigating in vitro neurodynamics in confined interconnected sub-populations of neurons. Sensors and Actuators B: Chemical, 114, 530–541.

    Article  CAS  Google Scholar 

  • Berenyi, A., Belluscio, M., Mao, D., & Buzsaki, G. (2012). Closed-loop control of epilepsy by transcranial electrical stimulation. Science, 337, 735–737.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Binczak, S., Jacquir, S., Bilbault, J. M., Kazantsev, V. B., & Nekorkin, V. I. (2006). Experimental study of electrical FitzHugh-Nagumo neurons with modified excitability. Neural Networks, 19, 684–693.

    Article  PubMed  Google Scholar 

  • Bisio, M., Bosca, A., Pasquale, V., Berdondini, L., & Chiappalone, M. (2014). Emergence of bursting activity in connected neuronal sub-populations. PLoS One, 9, e107400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Blaha, C. D., & Phillips, A. G. (1996). A critical assessment of electrochemical procedures applied to the measurement of dopamine and its metabolites during drug-induced and species-typical behaviours. Behavioural Pharmacology, 7, 675–708.

    Article  CAS  PubMed  Google Scholar 

  • Bonabi, S. Y., Asgharian, H., Bakhtiari, R., Safari, S. & Ahmadabadi, M. N. (2012). FPGA implementation of a cortical network based on the Hodgkin-Huxley neuron model. International Conference on Neural Information Processing (pp. 243–250). Berlin: Springer.

    Google Scholar 

  • Bonifazi, P., Difato, F., Massobrio, P., Breschi, G. L., Pasquale, V., Levi, T., et al. (2013). In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses. Front Neural Circuits, 7, 40.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bonifazi, P., Ruaro, M. E., & Torre, V. (2005). Statistical properties of information processing in neuronal networks. European Journal of Neuroscience, 22, 2953–2964.

    Article  Google Scholar 

  • Boucsein, C., Nawrot, M., Schnepel, P., & Aertsen, A. (2011). Beyond the cortical column: Abundance and physiology of horizontal connections imply a strong role for inputs from the surround. Frontiers in Neuroscience, 5, 32.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bouton, C. E., Shaikhouni, A., Annetta, N. V., Bockbrader, M. A., Friedenberg, D. A., Nielson, D. M., et al. (2016). Restoring cortical control of functional movement in a human with quadriplegia. Nature, 533, 247–250.

    Article  CAS  PubMed  Google Scholar 

  • Bovetti, S., & Fellin, T. (2015). Optical dissection of brain circuits with patterned illumination through the phase modulation of light. Journal of Neuroscience Methods, 241, 66–77.

    Article  PubMed  Google Scholar 

  • Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G., & Deisseroth, K. (2005). Millisecond-timescale, genetically targeted optical control of neural activity. Nature Neuroscience, 8, 1263.

    Article  CAS  PubMed  Google Scholar 

  • Boyn, S., Grollier, J., Lecerf, G., Xu, B., Locatelli, N., Fusil, S., et al. (2017). Learning through ferroelectric domain dynamics in solid-state synapses. Nature Communications, 8, 14736.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Broccard, F. D., Joshi, S., Wang, J., & Cauwenberghs, G. (2017). Neuromorphic neural interfaces: From neurophysiological inspiration to biohybrid coupling with nervous systems. Journal of Neural Engineering, 14, 041002.

    Article  PubMed  Google Scholar 

  • Bruzzone, A., Pasquale, V., Nowak, P., Tessadori, J., Massobrio, P., & Chiappalone, M. (2015). Interfacing in silico and in vitro neuronal networks. Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE (pp. 3391–3394). IEEE.

    Google Scholar 

  • Budiman, F., Hernowo, D. G. O., Pandey, R. R., & Tanaka, H. (2018). Recent progress on fabrication of memristor and transistor-based neuromorphic devices for high signal processing speed with low power consumption. Japanese Journal of Applied Physics, 57, 03EA06.

    Article  Google Scholar 

  • Buzsaki, G. (2010). Neural syntax: Cell assemblies, synapsembles, and readers. Neuron, 68, 362–385.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cagnan, H., Pedrosa, D., Little, S., Pogosyan, A., Cheeran, B., Aziz, T., et al. (2017). Stimulating at the right time: Phase-specific deep brain stimulation. Brain, 140, 132–145.

    Article  PubMed  Google Scholar 

  • Capogrosso, M., Milekovic, T., Borton, D., Wagner, F., Moraud, E. M., Mignardot, J.-B., et al. (2016). A brain–spine interface alleviating gait deficits after spinal cord injury in primates. Nature, 539, 284.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cassidy, A., Andreou, A. G. & Georgiou, J. (2011) Design of a one million neuron single FPGA neuromorphic system for real-time multimodal scene analysis. Information Sciences and Systems (CISS), 45th Annual Conference on. (pp. 1–6). IEEE.

    Google Scholar 

  • Chapin, J. K., Moxon, K. A., Markowitz, R. S., & Nicolelis, M. A. L. (1999). Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nature Neuroscience, 2, 664–670.

    Article  CAS  PubMed  Google Scholar 

  • Chiappalone, M., Bove, M., Vato, A., Tedesco, M., & Martinoia, S. (2006). Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development. Brain Research, 1093, 41–53.

    Article  CAS  PubMed  Google Scholar 

  • Chiappalone, M., Massobrio, P., & Martinoia, S. (2008). Network plasticity in cortical assemblies. The European Journal of Neuroscience, 28, 221–237.

    Article  PubMed  Google Scholar 

  • Chiolerio, A., Chiappalone, M., Ariano, P., & Bocchini, S. (2017). Coupling resistive switching devices with neurons: State of the art and perspectives. Frontiers in Neuroscience, 11, 70.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chou, Z., Lim, J., Brown, S., Keller, M., Bugbee, J., Broccard, F. D., et al. (2015). Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems. Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE (pp. 3949–3952). IEEE.

    Google Scholar 

  • Daly, J. J., & Wolpaw, J. R. (2008). Brain-computer interfaces in neurological rehabilitation. Lancet Neurology, 7, 1032–1043.

    Article  PubMed  Google Scholar 

  • DeMarse, T. B. & Dockendorf, K. P. (2005). Adaptive flight control with living neuronal networks on microelectrode arrays. Neural Networks. IJCNN’05. Proceedings. 2005 IEEE International Joint Conference on. (Vol. 3, pp. 1548–1551). IEEE.

    Google Scholar 

  • Demarse, T. B., Wagenaar, D. A., Blau, A. W., & Potter, S. M. (2001). The neurally controlled animat: Biological brains acting with simulated bodies. Autonomous Robots, 11, 305–310.

    Article  PubMed  PubMed Central  Google Scholar 

  • Derdikman, D., Hildesheim, R., Ahissar, E., Arieli, A., & Grinvald, A. (2003). Imaging spatiotemporal dynamics of surround inhibition in the barrels somatosensory cortex. The Journal of Neuroscience, 23, 3100–3105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Downey, J. E., Weiss, J. M., Muelling, K., Venkatraman, A., Valois, J. S., Hebert, M., et al. (2016). Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. Journal of Neuroengineering and Rehabilitation, 13, 28.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ethier, C., Oby, E. R., Bauman, M. J., & Miller, L. E. (2012). Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature, 485, 368–371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Eytan, D., & Marom, S. (2006). Dynamics and effective topology underlying synchronization in networks of cortical neurons. Journal of Neuroscience, 26, 8465–8476.

    Article  CAS  PubMed  Google Scholar 

  • Fernandez-Vargas, J., Pfaff, H. U., Rodriguez, F. B., & Varona, P. (2013). Assisted closed-loop optimization of SSVEP-BCI efficiency. Front Neural Circuits, 7, 27.

    Article  PubMed  PubMed Central  Google Scholar 

  • Flesher, S. N., Collinger, J. L., Foldes, S. T., Weiss, J. M., Downey, J. E., Tyler-Kabara, E. C., et al. (2016). Intracortical microstimulation of human somatosensory cortex. Science Translational Medicine, 8(361), 361ra141.

    Article  PubMed  Google Scholar 

  • Fong, M.-F., Newman, J. P., Potter, S. M., & Wenner, P. (2015). Upward synaptic scaling is dependent on neurotransmission rather than spiking. Nature Communications, 6, 6339.

    Article  CAS  PubMed  Google Scholar 

  • Furber, S. B., Lester, D. R., Plana, L. A., Garside, J. D., Painkras, E., Temple, S., et al. (2013). Overview of the SpiNNaker system architecture. IEEE Transactions on Computers, 62, 2454–2467.

    Article  Google Scholar 

  • Gal, A., Eytan, D., Wallach, A., Sandler, M., Schiller, J., & Marom, S. (2010). Dynamics of excitability over extended timescales in cultured cortical neurons. The Journal of Neuroscience, 30, 16332–16342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Galvani, L., & Aldini, G. (1792). De viribus electricitatis in motu musculari comentarius cum joannis aldini dissertatione et notis; accesserunt epistolae ad animalis electricitatis theoriam pertinentes. Apud Societatem Typographicam.

    Google Scholar 

  • George, S., Hasler, J., Koziol, S., Nease, S., & Ramakrishnan, S. (2013). Low power dendritic computation for wordspotting. Journal of Low Power Electronics Applications, 3, 73–98.

    Article  Google Scholar 

  • Gewaltig, M.-O., & Diesmann, M. (2007). Nest (neural simulation tool). Scholarpedia, 2, 1430.

    Article  Google Scholar 

  • Goodman, D. F., & Brette, R. (2009). The brian simulator. Frontiers in Neuroscience, 3, 192–197.

    Article  PubMed  PubMed Central  Google Scholar 

  • Grahn, P. J., Mallory, G. W., Berry, B. M., Hachmann, J. T., Lobel, D. A., & Lujan, J. L. (2014). Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: Toward a next generation closed-loop neural prosthesis. Frontiers in Neuroscience, 8, 296.

    PubMed  PubMed Central  Google Scholar 

  • Grassia, F., Buhry, L., Levi, T., Tomas, J., Destexhe, A., & Saighi, S. (2011). Tunable neuromimetic integrated system for emulating cortical neuron models. Frontiers in Neuroscience, 5, 134.

    Article  PubMed  PubMed Central  Google Scholar 

  • Greenwald, E., Masters, M. R., & Thakor, N. V. (2016). Implantable neurotechnologies: Bidirectional neural interfaces—applications and VLSI circuit implementations. Medical & Biological Engineering & Computing, 54, 1–17.

    Article  Google Scholar 

  • Guggenmos, D. J., Azin, M., Barbay, S., Mahnken, J. D., Dunham, C., Mohseni, P., et al. (2013). Restoration of function after brain damage using a neural prosthesis. Proceedings of the National Academy of Sciences of the United States of America, 110, 21177–21182.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gustafsson, B., & Jankowska, E. (1976). Direct and indirect activation of nerve cells by electrical pulses applied extracellularly. The Journal of Physiology, 258, 33–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gustavsson, A., Svensson, M., Jacobi, F., Allgulander, C., Alonso, J., Beghi, E., et al. (2011). Cost of disorders of the brain in Europe 2010. European Neuropsychopharmacology, 21, 718–779.

    Article  CAS  PubMed  Google Scholar 

  • Gwatkin, D. R., Guillot, M., & Heuveline, P. (1999). The burden of disease among the global poor. Lancet, 354, 586–589.

    Article  CAS  PubMed  Google Scholar 

  • Hasler, P., Kozoil, S., Farquhar, E. & Basu, A. (2007). Transistor channel dendrites implementing HMM classifiers. 2007 IEEE International Symposium on Circuits and Systems (vols. 1–11, p. 3359).

    Google Scholar 

  • Hazan, H., & Ziv, N. E. (2017). Closed Loop Experiment Manager (CLEM)—an open and inexpensive solution for multichannel electrophysiological recordings and closed loop experiments. Frontiers in Neuroscience, 11, 579.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hines, M. L., & Carnevale, N. T. (2001). NEURON: A tool for neuroscientists. The Neuroscientist, 7, 123–135.

    Article  CAS  PubMed  Google Scholar 

  • Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., et al. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485, 372–375.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Holinski, B. J., Mazurek, K. A., Everaert, D. G., Stein, R. B., & Mushahwar, V. K. (2011). Restoring stepping after spinal cord injury using intraspinal microstimulation and novel control strategies. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Embc), 2011, 5798–5801.

    Google Scholar 

  • Hubel, D. H., Wiesel, T. N., & LeVay, S. (1977). Plasticity of ocular dominance columns in monkey striate cortex. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 278, 377–409.

    Article  CAS  PubMed  Google Scholar 

  • Idelson, M. S., Ben-Jacob, E., & Hanein, Y. (2010). Innate synchronous oscillations in freely-organized small neuronal circuits. PLoS One, 5, e14443.

    Article  CAS  Google Scholar 

  • Indiveri, G., & Fusi, S. (2007). Spike-based learning in VLSI networks of integrate- and-fire neurons. IEEE International Symposium on Circuits and Systems, 1–11, 3371–3374.

    Google Scholar 

  • Indiveri, G., Linares-Barranco, B., Hamilton, T. J., Van Schaik, A., Etienne-Cummings, R., Delbruck, T., et al. (2011). Neuromorphic silicon neuron circuits. Frontiers in Neuroscience, 5, 73.

    PubMed  PubMed Central  Google Scholar 

  • Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569–1572.

    Article  CAS  PubMed  Google Scholar 

  • Jackson, A., Mavoori, J., & Fetz, E. E. (2006). Long-term motor cortex plasticity induced by an electronic neural implant. Nature, 444, 56–60.

    Article  CAS  PubMed  Google Scholar 

  • Jankowska, E., & Roberts, W. J. (1972a). An electrophysiological demonstration of the axonal projections of single spinal interneurones in the cat. The Journal of Physiology, 222, 597–622.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jankowska, E., & Roberts, W. J. (1972b). Synaptic actions of single interneurones mediating reciprocal Ia inhibition of motoneurones. The Journal of Physiology, 222, 623–642.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jimbo, Y., Robinson, H. P. C., & Kawana, A. (1998). Strengthening of synchronized activity by tetanic stimulation in cortical cultures: Application of planar electrode arrays. IEEE Transactions on Biomedical Engineering, 45, 1297–1304.

    Article  CAS  PubMed  Google Scholar 

  • Jordan, J., Ippen, T., Helias, M., Kitayama, I., Sato, M., Igarashi, J., et al. (2018). Extremely scalable spiking neuronal network simulation code: From laptops to exascale computers. Frontiers in Neuroinformatics, 12, 2.

    Article  PubMed  PubMed Central  Google Scholar 

  • Joucla, S., Ambroise, M., Levi, T., Lafon, T., Chauvet, P., Saighi, S., et al. (2016). Generation of locomotor-like activity in the isolated rat spinal cord using intraspinal electrical microstimulation driven by a digital neuromorphic CPG. Frontiers in Neuroscience, 10, 67.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ju, H., Dranias, M. R., Banumurthy, G., & VanDongen, A. M. (2015). Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons. Journal of Neuroscience, 35, 4040–4051.

    Article  CAS  PubMed  Google Scholar 

  • Jung, R., Brauer, E. J., & Abbas, J. J. (2001). Real-time interaction between a neuromorphic electronic circuit and the spinal cord. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9, 319–326.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jung, R., Kiemel, T., & Cohen, A. H. (1996). Dynamic behavior of a neural network model of locomotor control in the lamprey. Journal of Neurophysiology, 75, 1074–1086.

    Article  CAS  PubMed  Google Scholar 

  • Karniel, A., Kositsky, M., Fleming, K. M., Chiappalone, M., Sanguineti, V., Alford, S. T., et al. (2005). Computational analysis in vitro: Dynamics and plasticity of a neuro-robotic system. Journal of Neural Engineering, 2, S250.

    Article  PubMed  Google Scholar 

  • Kasten, M. R., Sunshine, M. D., Secrist, E. S., Horner, P. J., & Moritz, C. T. (2013). Therapeutic intraspinal microstimulation improves forelimb function after cervical contusion injury. Journal of Neural Engineering, 10, 044001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Keren, H., & Marom, S. (2014). Controlling neural network responsiveness: Tradeoffs and constraints. Frontiers in Neuroengineering, 7, 11.

    PubMed  PubMed Central  Google Scholar 

  • Kohno, T., Sekikawa, M., Li, J., Nanami, T., & Aihara, K. (2016). Qualitative-modeling-based silicon neurons and their networks. Frontiers in Neuroscience, 10, 273.

    PubMed  PubMed Central  Google Scholar 

  • Kositsky, M., Chiappalone, M., Alford, S. T., & Mussa-Ivaldi, F. A. (2009). Brain-machine interactions for assessing the dynamics of neural systems. Frontiers in Neurorobotics, 3, 1.

    Article  PubMed  PubMed Central  Google Scholar 

  • Krook-Magnuson, E., Armstrong, C., Oijala, M., & Soltesz, I. (2013). On-demand optogenetic control of spontaneous seizures in temporal lobe epilepsy. Nature Communications, 4, 1376.

    Article  CAS  PubMed  Google Scholar 

  • Kudoh, S. N., Tokuda, M., Kiyohara, A., Hosokawa, C., Taguchi, T., & Hayashi, I. (2011). Vitroid—the robot system with an interface between a living neuronal network and outer world. International Journal of Mechatronics and Manufacturing Systems, 4, 135–149.

    Article  Google Scholar 

  • Kumar, A., Rotter, S., & Aertsen, A. (2010). Spiking activity propagation in neuronal networks: Reconciling different perspectives on neural coding. Nature Reviews. Neuroscience, 11, 615–627.

    Article  CAS  PubMed  Google Scholar 

  • Kumar, S. S., Wulfing, J., Okujeni, S., Boedecker, J., Riedmiller, M., & Egert, U. (2016). Autonomous optimization of targeted stimulation of neuronal networks. PLoS Computational Biology, 12, e1005054.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kunkel, S., Schmidt, M., Eppler, J. M., Plesser, H. E., Masumoto, G., Igarashi, J., et al. (2014). Spiking network simulation code for petascale computers. Frontiers in Neuroinformatics, 8, 78.

    Article  PubMed  PubMed Central  Google Scholar 

  • le Feber, J., Stegenga, J., & Rutten, W. L. (2010). The effect of slow electrical stimuli to achieve learning in cultured networks of rat cortical neurons. PLoS One, 5, e8871.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • le Feber, J., van Pelt, J., & Rutten, W. (2007). Latency dependent development of related firing patterns of cultured cortical neurons. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, 3000–3003.

    Google Scholar 

  • le Feber, J., Witteveen, T., van Veenendaal, T. M., & Dijkstra, J. (2015). Repeated stimulation of cultured networks of rat cortical neurons induces parallel memory traces. Learning & Memory, 22, 594–603.

    Article  Google Scholar 

  • Le Masson, G., Renaud-Le Masson, S., Debay, D., & Bal, T. (2002). Feedback inhibition controls spike transfer in hybrid thalamic circuits. Nature, 417, 854–858.

    Article  CAS  PubMed  Google Scholar 

  • Lee, K. J., Jang, K. S., & Shon, Y. M. (2006). Chronic deep brain stimulation of subthalamic and anterior thalamic nuclei for controlling refractory partial epilepsy. Acta Neurochirurgica. Supplement, 99, 87–91.

    Article  CAS  PubMed  Google Scholar 

  • Levi, T., Bonifazi, P., Massobrio, P., & Chiappalone, M. (2018a). Editorial: Closed-loop systems for next-generation neuroprostheses. Frontiers in Neuroscience, 12, 26.

    Article  PubMed  PubMed Central  Google Scholar 

  • Levi, T., Khoyratee, F., Saïghi, S., & Ikeuchi, Y. (2018b). Digital implementation of Hodgkin–Huxley neuron model for neurological diseases studies. Artificial Life Robotics, 23, 10–14.

    Article  Google Scholar 

  • Levi, T., Lewis, N., Saïghi, S., Tomas, J., Bornat, Y. & Renaud, S. (2008). Neuromimetic integrated circuits. In Iniewski, K. (ed) VLSI Circuits for Biomedical Applications. Artech House, 241–264.

    Google Scholar 

  • Levi, T., Nanami, T., Tange, A., Aihara, K., & Kohno, T. (2018). Development and applications of biomimetic neuronal networks toward brainmorphic artificial intelligence. IEEE Transactions on Circuits and Systems II: Express Briefs, 65, 577–581.

    Article  Google Scholar 

  • Levy, O., Ziv, N. E., & Marom, S. (2012). Enhancement of neural representation capacity by modular architecture in networks of cortical neurons. European Journal of Neuroscience, 35, 1753–1760.

    Article  Google Scholar 

  • Li, Y., Sun, R., Wang, Y., Li, H., & Zheng, X. (2016). A novel robot system integrating biological and mechanical intelligence based on dissociated neural network-controlled closed-loop environment. PLoS One, 11, e0165600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li, Y. L., Zhou, W., Li, X. N., Zeng, S. Q., Liu, M., & Luo, Q. M. (2007). Characterization of synchronized bursts in cultured hippocampal neuronal networks with learning training on microelectrode arrays. Biosensors and Bioelectronics, 22, 2976–2982.

    Article  CAS  PubMed  Google Scholar 

  • Little, S., Pogosyan, A., Neal, S., Zavala, B., Zrinzo, L., Hariz, M., et al. (2013). Adaptive deep brain stimulation in advanced Parkinson disease. Annals of Neurology, 74, 449–457.

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu, S. C., & Douglas, R. (2004). Temporal coding in a silicon network of integrate-and-fire neurons. IEEE Transactions on Neural Networks, 15, 1305–1314.

    Article  PubMed  Google Scholar 

  • Lobov, S., Balashova, K., Makarov, V., & Kazantsev, V. (2017). Competition of spike-conducting pathways in STDP driven neural networks. Proceedings of the 5th International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2017), 15–21. https://doi.org/10.5220/0006497400150021

  • Macis, E., Tedesco, M., Massobrio, P., Raiteri, R., & Martinoia, S. (2007). An automated microdrop delivery system for neuronal network patterning on microelectrode arrays. Journal of Neuroscience Methods, 161, 88–95.

    Article  PubMed  Google Scholar 

  • Madisen, L., Mao, T., Koch, H., Zhuo, J.-M., Berenyi, A., Fujisawa, S., et al. (2012). A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nature Neuroscience, 15, 793.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mahowald, M., & Douglas, R. (1991). A silicon neuron. Nature, 354, 515–518.

    Article  CAS  PubMed  Google Scholar 

  • Mak, J. N., & Wolpaw, J. R. (2009). Clinical applications of brain-computer interfaces: Current state and future prospects. IEEE Reviews in Biomedical Engineering, 2, 187–199.

    Article  PubMed  PubMed Central  Google Scholar 

  • Markram, H. (2012). The human brain project. Scientific American, 306, 50–55.

    Article  PubMed  Google Scholar 

  • Marom, S., & Eytan, D. (2005). Learning in ex-vivo developing networks of cortical neurons. Progress in Brain Research, 147, 189–199.

    Article  PubMed  Google Scholar 

  • Marom, S., & Shahaf, G. (2002). Development, learning and memory in large random networks of cortical neurons: Lessons beyond anatomy. Quarterly Reviews of Biophysics, 35, 63–87.

    Article  PubMed  Google Scholar 

  • Martinoia, S., Massobrio, P., Bove, M., & Massobrio, G. (2004a). Cultured neurons coupled to microelectrode arrays: Circuit models, simulations and experimental data. IEEE Transactions on Biomedical Engineering, 51, 859–863.

    Article  PubMed  Google Scholar 

  • Martinoia, S., Sanguineti, V., Cozzi, L., Berdondini, L., Van Pelt, J., Tomas, J., et al. (2004b). Towards an embodied in vitro electrophysiology: The NeuroBIT project. Neurocomputing, 58, 1065–1072.

    Article  Google Scholar 

  • Massobrio, P., Tessadori, J., Chiappalone, M., & Ghirardi, M. (2015). In vitro studies of neuronal networks and synaptic plasticity in invertebrates and in mammals using multielectrode arrays. Neural Plasticity, 2015, 196195.

    Article  PubMed  PubMed Central  Google Scholar 

  • Masumori, A., Sinapayen, L., Maruyama, N., Mita, T., Bakkum, D. J., Frey, U., et al. (2018). Autonomous regulation of self and non-self by stimulation avoidance in embodied neural networks. Artificial Life Conference Proceedings (pp. 163–170). Cambridge: MIT Press.

    Google Scholar 

  • Mattis, J., Tye, K. M., Ferenczi, E. A., Ramakrishnan, C., O’shea, D. J., Prakash, R., et al. (2012). Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins. Nature Methods, 9, 159.

    Article  CAS  Google Scholar 

  • Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4, 200.

    Article  PubMed  PubMed Central  Google Scholar 

  • Miao, Y., & Koomson, V. J. (2018). A CMOS-based bidirectional brain machine interface system with integrated fdNIRS and tDCS for closed-loop brain stimulation. IEEE Transactions on Biomedical Circuits and Systems, 12, 554–563.

    Article  PubMed  Google Scholar 

  • Millan, J. D., Rupp, R., Muller-Putz, G. R., Murray-Smith, R., Giugliemma, C., Tangermann, M., et al. (2010). Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges. Frontiers in Neuroscience, 4, 161.

    PubMed  PubMed Central  Google Scholar 

  • Molinuevo, J. L., Valldeoriola, F., Tolosa, E., Rumia, J., Valls-Sole, J., Roldan, H., et al. (2000). Levodopa withdrawal after bilateral subthalamic nucleus stimulation in advanced Parkinson disease. Archives of Neurology, 57, 983–988.

    Article  CAS  PubMed  Google Scholar 

  • Morgante, L., Morgante, F., Moro, E., Epifanio, A., Girlanda, P., Ragonese, P., et al. (2007). How many parkinsonian patients are suitable candidates for deep brain stimulation of subthalamic nucleus? Results of a questionnaire. Parkinsonism & Related Disorders, 13, 528–531.

    Article  Google Scholar 

  • Moro, E., Scerrati, M., Romito, L. M., Roselli, R., Tonali, P., & Albanese, A. (1999). Chronic subthalamic nucleus stimulation reduces medication requirements in Parkinson’s disease. Neurology, 53, 85–90.

    Article  CAS  PubMed  Google Scholar 

  • Moxon, K. A., & Foffani, G. (2015). Brain-machine interfaces beyond neuroprosthetics. Neuron, 86, 55–67.

    Article  CAS  PubMed  Google Scholar 

  • Muller, J., Bakkum, D. J., & Hierlemann, A. (2013). Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons. Frontiers in Neural Circuits, 6, 121.

    Article  PubMed  PubMed Central  Google Scholar 

  • Murray, J. B. (1996). Psychophysiological aspects of autistic disorders: Overview. The Journal of Psychology, 130, 145–158.

    Article  CAS  PubMed  Google Scholar 

  • Mushahwar, V. K., Gillard, D. M., Gauthier, M. J., & Prochazka, A. (2002). Intraspinal micro stimulation generates locomotor-like and feedback-controlled movements. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 10, 68–81.

    Article  PubMed  Google Scholar 

  • Mussa-Ivaldi, S., Alford, S. T., Chiappalone, M., Fadiga, L., Karniel, A., Kositsky, M., et al. (2010). New perspectives on the dialogue between brains and machines. Frontiers in Neuroscience, 3, 8.

    Google Scholar 

  • Nanami, T., & Kohno, T. (2016). Simple cortical and thalamic neuron models for digital arithmetic circuit implementation. Frontiers in Neuroscience, 10, 181.

    Article  PubMed  PubMed Central  Google Scholar 

  • Natarajan, A., & Hasler, J. (2018). Hodgkin-Huxley neuron and FPAA dynamics. IEEE Transactions on Biomedical Circuits and Systems, 12, 918–926.

    Article  PubMed  Google Scholar 

  • Navarro, X., Krueger, T. B., Lago, N., Micera, S., Stieglitz, T., & Dario, P. (2005). A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. Journal of the Peripheral Nervous System, 10, 229–258.

    Article  PubMed  Google Scholar 

  • Newman, J. P., Zeller-Townson, R., Fong, M. F., Desai, S. A., Gross, R. E., & Potter, S. M. (2013). Closed-loop, multichannel experimentation using the open-source NeuroRighter electrophysiology platform. Frontiers in Neural Circuits, 6, 98.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nishimura, Y., Perlmutter, S. I., & Fetz, E. E. (2013). Restoration of upper limb movement via artificial corticospinal and musculospinal connections in a monkey with spinal cord injury. Front Neural Circuits, 7, 57.

    Article  PubMed  PubMed Central  Google Scholar 

  • Novellino, A., D’Angelo, P., Cozzi, L., Chiappalone, M., Sanguineti, V., & Martinoia, S. (2007). Connecting neurons to a mobile robot: An in vitro bidirectional neural interface. Computational Intelligence and Neuroscience, 2007, 12725.

    Article  PubMed Central  Google Scholar 

  • O’connor, D. H., Hires, S. A., Guo, Z. V., Li, N., Yu, J., Sun, Q.-Q., et al. (2013). Neural coding during active somatosensation revealed using illusory touch. Nature Neuroscience, 16, 958.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Opris, I., Fuqua, J. L., Huettl, P. F., Gerhardt, G. A., Berger, T. W., Hampson, R. E., et al. (2012). Closing the loop in primate prefrontal cortex: Inter-laminar processing. Front Neural Circuits, 6, 88.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pan, R. K., Chatterjee, N., & Sinha, S. (2010). Mesoscopic organization reveals the constraints governing Caenorhabditis elegans nervous system. PLoS One, 5, e9240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Panuccio, G., Semprini, M., Natale, L., Buccelli, S., Colombi, I. & Chiappalone, M. (2018). Progress in neuroengineering for brain repair: New challenges and open issues. Brain and Neuroscience Advances, 2. https://doi.org/10.1177/2398212818776475.

  • Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E., & Fellin, T. (2017). Cracking the neural code for sensory perception by combining statistics, intervention, and behavior. Neuron, 93, 491–507.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Panzeri, S., Ince, R. A. A., Diamond, M. E., & Kayser, C. (2014). Reading spike timing without a clock: Intrinsic decoding of spike trains. Philosophical Transactions of the Royal Society, B: Biological Sciences, 369, 20120467.

    Article  PubMed Central  Google Scholar 

  • Pasquale, V., Martinoia, S., & Chiappalone, M. (2017). Stimulation triggers endogenous activity patterns in cultured cortical networks. Scientific Reports, 7, 9080.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Paz, J. T., Davidson, T. J., Frechette, E. S., Delord, B., Parada, I., Peng, K., et al. (2013). Closed-loop optogenetic control of thalamus as a tool for interrupting seizures after cortical injury. Nature Neuroscience, 16, 64.

    Article  CAS  PubMed  Google Scholar 

  • Pikov, V., Bullara, L., & McCreery, D. B. (2007). Intraspinal stimulation for bladder voiding in cats before and after chronic spinal cord injury. Journal of Neural Engineering, 4, 356.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pimashkin, A., Gladkov, A., Agrba, E., Mukhina, I., & Kazantsev, V. (2016). Selectivity of stimulus induced responses in cultured hippocampal networks on microelectrode arrays. Cognitive Neurodynamics, 10, 287–299.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pimashkin, A., Gladkov, A., Mukhina, I., & Kazantsev, V. (2013). Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays. Front Neural Circuits, 7, 87.

    Article  PubMed  PubMed Central  Google Scholar 

  • Poli, D., Thiagarajan, S., DeMarse, T. B., Wheeler, B. C., & Brewer, G. J. (2017). Sparse and specific coding during information transmission between co-cultured dentate gyrus and CA3 hippocampal networks. Frontiers in Neural Circuits, 11, 13.

    Article  PubMed  PubMed Central  Google Scholar 

  • Potter, S. M. (2010). Closing the loop between neurons and neurotechnology. Frontiers in Neuroscience, 4, 15.

    Article  PubMed  PubMed Central  Google Scholar 

  • Potter, S. M., El Hady, A., & Fetz, E. E. (2014). Closed-loop neuroscience and neuroengineering. Frontiers in Neural Circuits, 8, 115.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pulizzi, R., Musumeci, G., Van den Haute, C., Van De Vijver, S., Baekelandt, V., & Giugliano, M. (2016). Brief wide-field photostimuli evoke and modulate oscillatory reverberating activity in cortical networks. Scientific Reports, 6, 24701.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., et al. (2015). A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Frontiers in Neuroscience, 9, 141.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rast, A. D., Partzsch, J., Mayr, C., Schemmel, J., Hartmann, S., Plana, L. A., et al. (2013). A location-independent direct link neuromorphic interface. 2013 International Joint Conference on Neural Networks (Ijcnn).

    Google Scholar 

  • Reger, B. D., Fleming, K. M., Sanguineti, V., Alford, S., & Mussa-Ivaldi, F. A. (2000). Connecting brains to robots: An artificial body for studying the computational properties of neural tissues. Artificial Life, 6, 307–324.

    Article  CAS  PubMed  Google Scholar 

  • Renaud, S., Tomas, J., Bornat, Y., Daouzli, A., & Saighi, S. (2007). Neuromimetic ICs with analog cores: An alternative for simulating spiking neural networks. IEEE International Symposium on Circuits and Systems, 1–11, 3355–3358.

    Google Scholar 

  • Renshaw, B. (1946). Observations on interaction of nerve impulses in the gray matter and on the nature of central inhibition. The American Journal of Physiology, 146, 443–448.

    Article  CAS  PubMed  Google Scholar 

  • Rey, H. G., Ahmadi, M., & Quian Quiroga, R. (2015). Single trial analysis of field potentials in perception, learning and memory. Current Opinion in Neurobiology, 31, 148–155.

    Article  CAS  PubMed  Google Scholar 

  • Rice, K. L., Bhuiyan, M. A., Taha, T. M., Vutsinas, C. N. & Smith, M. C. (2009). FPGA implementation of Izhikevich spiking neural networks for character recognition. 2009 International Conference on Reconfigurable Computing and Fpgas (p. 451).

    Google Scholar 

  • Rieke, F., Warland, D., de Ruyter van Steveninck, R., & Bialek, W. (1997). Spikes (p. 3). Cambridge: Massachusetts Institute of Technology.

    Google Scholar 

  • Robinson, J. T., Jorgolli, M., & Park, H. (2013). Nanowire electrodes for high-density stimulation and measurement of neural circuits. Front Neural Circuits, 7, 38.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rolston, J. D., Gross, R. E., & Potter, S. M. (2009). NeuroRighter: Closed-loop multielectrode stimulation and recording for freely moving animals and cell cultures. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, 6489–6492.

    Google Scholar 

  • Rosin, B., Slovik, M., Mitelman, R., Rivlin-Etzion, M., Haber, S. N., Israel, Z., et al. (2011). Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron, 72, 370–384.

    Article  CAS  PubMed  Google Scholar 

  • Sabarad, J., Kestur, S., Park, M. S., Dantara, D., Narayanan, V., Chen, Y., et al. (2012) A reconfigurable accelerator for neuromorphic object recognition. 2012 17th Asia and South Pacific Design Automation Conference (Asp-Dac) (pp. 813–818).

    Google Scholar 

  • Sartorius, N., & Henderson, A. S. (1992). The neglect of prevention in psychiatry. The Australian and New Zealand Journal of Psychiatry, 26, 550–553.

    Article  CAS  PubMed  Google Scholar 

  • Schemmel, J., Bruderle, D., Meier, K., & Ostendorf, B. (2007). Modeling synaptic plasticity within networks of highly accelerated I&F neurons. IEEE International Symposium on Circuits and Systems, 1–11, 3367–3370.

    Google Scholar 

  • Schwartz, A. B. (2016). Movement: How the brain communicates with the world. Cell, 164, 1122–1135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shahaf, G., Eytan, D., Gal, A., Kermany, E., Lyakhov, V., Zrenner, C., et al. (2008). Order-based representation in random networks of cortical neurons. PLoS Computational Biology, 4, e1000228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shahaf, G., & Marom, S. (2001). Learning in networks of cortical neurons. The Journal of Neuroscience, 21, 8782–8788.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shein-Idelson, M., Ben-Jacob, E., & Hanein, Y. (2011). Engineered neuronal circuits: A new platform for studying the role of modular topology. Frontiers in Neuroengineering, 4, 10.

    Article  PubMed  PubMed Central  Google Scholar 

  • Siegle, J. H., Hale, G. J., Newman, J. P., & Voigts, J. (2015). Neural ensemble communities: Open-source approaches to hardware for large-scale electrophysiology. Current Opinion in Neurobiology, 32, 53–59.

    Article  CAS  PubMed  Google Scholar 

  • Silva, G. A. (2018). A new frontier: The convergence of nanotechnology, brain machine interfaces, and artificial intelligence. Frontiers in Neuroscience, 12, 843.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sinapayen, L., Masumori, A., & Ikegami, T. (2017). Learning by stimulation avoidance: A principle to control spiking neural networks dynamics. PLoS One, 12, e0170388.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sisterson, N. D., Wozny, T. A., Kokkinos, V., Constantino, A., & Richardson, R. M. (2019). Closed-loop brain stimulation for drug-resistant epilepsy: Towards an evidence-based approach to personalized medicine. Neurotherapeutics, 16, 119–127.

    Article  PubMed  Google Scholar 

  • Sporns, O., Tononi, G., & Edelman, G. M. (2000). Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex, 10, 127–141.

    Article  CAS  PubMed  Google Scholar 

  • Stegenga, J., Le Feber, J., Marani, E., & Rutten, W. L. (2009). The effect of learning on bursting. IEEE Transactions on Biomedical Engineering, 56, 1220–1227.

    Article  PubMed  Google Scholar 

  • Sunshine, M. D., Cho, F. S., Lockwood, D. R., Fechko, A. S., Kasten, M. R., & Moritz, C. T. (2013). Cervical intraspinal microstimulation evokes robust forelimb movements before and after injury. Journal of Neural Engineering, 10(3), 036001.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tessadori, J., Bisio, M., Martinoia, S., & Chiappalone, M. (2012). Modular neuronal assemblies embodied in a closed-loop environment: Toward future integration of brains and machines. Front Neural Circuits, 6, 99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Turrigiano, G. (2012). Homeostatic synaptic plasticity: Local and global mechanisms for stabilizing neuronal function. Cold Spring Harbor Perspectives in Biology, 4, a005736.

    Article  PubMed  PubMed Central  Google Scholar 

  • Turrigiano, G. G., & Nelson, S. B. (2000). Hebb and homeostasis in neuronal plasticity. Current Opinion in Neurobiology, 10, 358–364.

    Article  CAS  PubMed  Google Scholar 

  • van Albada, S. J., Rowley, A. G., Senk, J., Hopkins, M., Schmidt, M., Stokes, A. B., et al. (2018). Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model. Frontiers in Neuroscience, 12, 291.

    Article  PubMed  PubMed Central  Google Scholar 

  • van den Brand, R., Heutschi, J., Barraud, Q., DiGiovanna, J., Bartholdi, K., Huerlimann, M., et al. (2012). Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science, 336, 1182–1185.

    Article  CAS  PubMed  Google Scholar 

  • van Pelt, J., Wolters, P. S., Corner, M. A., Rutten, W. L., & Ramakers, G. J. (2004). Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks. IEEE Transactions on Biomedical Engineering, 51, 2051–2062.

    Article  PubMed  Google Scholar 

  • Vassanelli, S., & Mahmud, M. (2016). Trends and challenges in neuroengineering: Toward “intelligent” neuroprostheses through brain-“brain inspired systems” communication. Frontiers in Neuroscience, 10, 438.

    Article  PubMed  PubMed Central  Google Scholar 

  • Vassileva, A., van Blooijs, D., Leijten, F., & Huiskamp, G. (2018). Neocortical electrical stimulation for epilepsy: Closed-loop versus open-loop. Epilepsy Research, 141, 95–101.

    Article  PubMed  Google Scholar 

  • Vogelstein, R. J., Mallik, U. & Cauwenberghs, G. (2004). Silicon spike-based synaptic array and address-event transceiver. Proceedings of the IEEE International Symposium on Circuits and Systems (Vol. 5, pp. 385–388).

    Google Scholar 

  • Wagenaar, D., DeMarse, T. B. & Potter, S. M. (2005a) MeaBench: A toolset for multi-electrode data acquisition and on-line analysis. Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on (pp. 518–521). IEEE.

    Google Scholar 

  • Wagenaar, D. A., Madhavan, R., Pine, J., & Potter, S. M. (2005b). Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation. The Journal of Neuroscience, 25, 680–688.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wagenaar, D. A., Pine, J., & Potter, S. M. (2006a). An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neuroscience, 7, 11.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wagenaar, D. A., Pine, J., & Potter, S. M. (2006b). Searching for plasticity in dissociated cortical cultures on multi-electrode arrays. Journal of Negative Results in Biomedicine, 5, 16.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wallach, A., Eytan, D., Gal, A., Zrenner, C., & Marom, S. (2011). Neuronal response clamp. Frontiers in Neuroengineering, 4, 3.

    Article  PubMed  PubMed Central  Google Scholar 

  • Walter, A., Ramos Murguialday, A., Spuler, M., Naros, G., Leao, M. T., Gharabaghi, A., et al. (2012). Coupling BCI and cortical stimulation for brain-state-dependent stimulation: Methods for spectral estimation in the presence of stimulation after-effects. Front Neural Circuits, 6, 87.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, R. C., Cohen, G., Stiefel, K. M., Hamilton, T. J., Tapson, J. T., & van Schaik, A. (2013a). An FPGA implementation of a polychronous spiking neural network with delay adaptation. Frontiers in Neuroscience, 7, 14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang, R. M., Cohen, G., Stiefel, K. M., Hamilton, T. J., Tapson, J. C., & van Schaik, A. (2013b). An FPGA implementation of a polychronous spiking neural network with delay adaptation. Frontiers in Neuroscience, 7, 14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Warwick, K., Xydas, D., Nasuto, S. J., Becerra, V. M., Hammond, M. W., Downes, J. H., et al. (2010). Controlling a mobile robot with a biological brain. Defence Science Journal, 60, 5–14.

    Article  Google Scholar 

  • Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113, 767–791.

    Article  PubMed  Google Scholar 

  • Wright, J., Macefield, V. G., van Schaik, A., & Tapson, J. C. (2016). A review of control strategies in closed-loop neuroprosthetic systems. Frontiers in Neuroscience, 10, 312.

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu, R., Jiang, N., Lin, C., Mrachacz-Kersting, N., Dremstrup, K., & Farina, D. (2014). Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications. IEEE Transactions on Biomedical Engineering, 61, 288–296.

    Article  PubMed  Google Scholar 

  • Yakovenko, S., Kowalczewski, J., & Prochazka, A. (2007). Intraspinal stimulation caudal to spinal cord transections in rats. Testing the propriospinal hypothesis. Journal of Neurophysiology, 97, 2570–2574.

    Article  PubMed  Google Scholar 

  • Zeng, H., Wang, Y., Wu, C., Song, A., Liu, J., Ji, P., et al. (2017). Closed-loop hybrid gaze brain-machine interface based robotic arm control with augmented reality feedback. Frontiers in Neurorobotics, 11, 60.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, F., Aravanis, A. M., Adamantidis, A., de Lecea, L., & Deisseroth, K. (2007). Circuit-breakers: Optical technologies for probing neural signals and systems. Nature Reviews Neuroscience, 8, 577.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, F., Vierock, J., Yizhar, O., Fenno, L. E., Tsunoda, S., Kianianmomeni, A., et al. (2011). The microbial opsin family of optogenetic tools. Cell, 147, 1446–1457.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang, Z., Russell, L. E., Packer, A. M., Gauld, O. M., & Häusser, M. (2018). Closed-loop all-optical interrogation of neural circuits in vivo. Nature Methods, 15(12), 1037–1040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Part of the presented research results has received funding from the European Union’s Seventh Framework Programme (ICT-FET FP7/2007-2013, FET Young Explorers scheme) under grant agreement n° 284772 BRAIN BOW (www.brainbowproject.eu). The authors would like to thank all the people who took part in the BrainBow project.

This work was also supported by the Russian Science Foundation (project No. 18--72--1004).

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Authors and Affiliations

  1. Department of Neurosciences, University of Padova, Padova, Italy

    Marta Bisio

  2. Department of Neurotechnologies, Lobachevsky State University of Nizhni Novgorod, Nizhnij Novgorod, Russia

    Alexey Pimashkin, Arseniy Gladkov, Irina Mukhina & Victor Kazantsev

  3. Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Genova, Italy

    Stefano Buccelli, Marianna Semprini, Ilaria Colombi, Alberto Averna & Michela Chiappalone

  4. Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy

    Stefano Buccelli, Ilaria Colombi, Valentina Pasquale & Michela Chiappalone

  5. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy

    Jacopo Tessadori

  6. Institute of Industrial Science, IIS, The University of Tokyo, Tokyo, Japan

    Timothée Levi

  7. IMS Lab, University of Bordeaux, Talence, France

    Timothée Levi

  8. Central Research Laboratory, Privolzhsky Research Medical University, Nizhnij Novgorod, Russia

    Arseniy Gladkov & Irina Mukhina

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  1. Rehab Technologies IIT-INAIL Joint Lab, Istituto Italiano di Tecnologia, Genova, Italy

    Michela Chiappalone

  2. Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy

    Valentina Pasquale

  3. Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands

    Monica Frega

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Bisio, M. et al. (2019). Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. In: Chiappalone, M., Pasquale, V., Frega, M. (eds) In Vitro Neuronal Networks. Advances in Neurobiology, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-11135-9_15

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