Publication: Spatial computing in structured spiking neural networks with a robotic embodiment
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2022
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Abstract
One of the challenges of modern neuroscience is creating a "living computer" based on neural networks grown in vitro. Such an artificial device is supposed to perform neurocomputational tasks and interact with the environment when embodied in a robot. Recent studies have identified the most critical challenge, the search for a neural network architecture to implement associative learning. This work proposes a model of modular architecture with spiking neural networks connected by unidirectional couplings. We show that the model enables training a neuro-robot according to Pavlovian conditioning. The robot's performance in obstacle avoidance depends on the ratio of the weights in inter-network couplings. We show that besides STDP, critical factors for successful learning are synaptic and neuronal competitions. We use the recently discovered shortest path rule to implement the synaptic competition. This method is ready for experimental testing. Strong inhibitory couplings implement the neuronal competition in the subnetwork responsible for the unconditional response. Empirical testing of this approach requires a technique for growing neural networks with a given ratio of excitatory and inhibitory neurons not available yet. An alternative is building a hybrid system with in vitro neural networks coupled through hardware memristive connections.
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Bakkum, D. J., Chao, Z. C., and Potter, S. M. (2008). Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task. J. Neural Eng. 5, 310. Available at: http://stacks.iop.org/1741-2552/5/i=3/a=004.
Bi, G. Q., and Poo, M. M. (1998). Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. J. Neurosci. 18, 10464 LP – 10472. doi:10.1038/25665.
Chua, L. O., and Kang, S. M. (1976). Memristive devices and systems. Proc. IEEE 64, 209–223. doi:10.1109/PROC.1976.10092.
Dauth, S., Maoz, B. M., Sheehy, S. P., Hemphill, M. A., Murty, T., Macedonia, M. K., et al. (2016). Neurons derived from different brain regions are inherently different in vitro: a novel multiregional brain-on-a-chip. J. Neurophysiol. 117, 1320–1341. doi:10.1152/jn.00575.2016.
Dias, C., Castro, D., Aroso, M., Ventura, J., and Aguiar, P. (2021). A memristor-based neuromodulation device for real-time monitoring and adaptive control of in vitro neuronal populations. bioRxiv.
Forró, C., Thompson-Steckel, G., Weaver, S., Weydert, S., Ihle, S., Dermutz, H., et al. (2018). Modular microstructure design to build neuronal networks of defined functional connectivity. Biosens. Bioelectron. 122, 75–87. doi:https://doi.org/10.1016/j.bios.2018.08.075.
Gladkov, A., Pigareva, Y., Kutyina, D., Kolpakov, V., Bukatin, A., Mukhina, I., et al. (2017). Design of Cultured Neuron Networks in vitro with Predefined Connectivity Using Asymmetric Microfluidic Channels. Sci. Rep. 7, 15625. doi:10.1038/s41598-017-15506-2.
Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Trans. Neural Networks 14, 1569–1572. doi:10.1109/TNN.2003.820440.
Izhikevich, E. M. (2004). Which model to use for cortical spiking neurons? IEEE Trans. Neural Networks 15, 1063–1070. doi:10.1109/TNN.2004.832719.
Izhikevich, E. M. (2007). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press.
Juzekaeva, E., Nasretdinov, A., Battistoni, S., Berzina, T., Iannotta, S., Khazipov, R., et al. (2019). Coupling Cortical Neurons through Electronic Memristive Synapse. Adv. Mater. Technol. 4, 1800350. doi:https://doi.org/10.1002/admt.201800350.
le Feber, J., Postma, W., de Weerd, E., Weusthof, M., and Rutten, W. L. C. (2015). Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays. Front. Neurosci. 9, 412. doi:10.3389/fnins.2015.00412.
Lobov, S. A., Chernyshov, A. V, Krilova, N. P., Shamshin, M. O., and Kazantsev, V. B. (2020a). Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.
Lobov, S. A., Mikhaylov, A. N., Shamshin, M., Makarov, V. A., and Kazantsev, V. B. (2020b). Spatial properties of STDP in a self-learning spiking neural network enable controlling a mobile robot. Front. Neurosci. 14, 88. doi:10.3389/fnins.2020.00088.
Lobov, S. A., Zhuravlev, M. O., Makarov, V. A., and Kazantsev, V. B. (2017a). Noise Enhanced Signaling in STDP Driven Spiking-Neuron Network. Math. Model. Nat. Phenom. 12, 109–124. doi:10.1051/mmnp/201712409.
Lobov, S., Balashova, K., Makarov, V. A., and Kazantsev, V. (2017b). Competition of Spike-Conducting Pathways in STDP Driven Neural Networks. in Proceedings of the 5th International Congress on Neurotechnology, Electronics and Informatics - NEUROTECHNIX, (SciTePress), 15–21. doi:10.5220/0006497400150021.
Malishev, E., Pimashkin, A., Arseniy, G., Pigareva, Y., Bukatin, A., Kazantsev, V., et al. (2015). Microfluidic device for unidirectional axon growth. doi:10.1088/1742-6596/643/1/012025.
Markram, H., Gerstner, W., and Sjöström, P. J. (2011). A history of spike-timing-dependent plasticity. Front. Synaptic Neurosci. 3, 4. doi:10.3389/fnsyn.2011.00004.
Markram, H., Lübke, J., Frotscher, M., and Sakmann, B. (1997). Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs. Science (80-. ). 275, 213 LP – 215. Available at: http://science.sciencemag.org/content/275/5297/213.abstract.
Meyer, J. A., and Wilson, S. W. (1991). From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior. MIT Press, Cambridge.
Mikhaylov, A., Pimashkin, A., Pigareva, Y., Gerasimova, S. A., Lobov, S., Gryaznov, E., et al. (2020). Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprostetics. Front. Neurosci. 14, 358.
Morrison, A., Diesmann, M., and Gerstner, W. (2008). Phenomenological models of synaptic plasticity based on spike timing. Biol. Cybern. 98, 459–478. doi:10.1007/s00422-008-0233-1.
Pamies, D., Hartung, T., and Hogberg, H. T. (2014). Biological and medical applications of a brain-on-a-chip. Exp. Biol. Med. 239, 1096–1107. doi:10.1177/1535370214537738.
Pan, L., Alagapan, S., Franca, E., DeMarse, T., Brewer, G. J., and Wheeler, B. C. (2014). Large Extracellular Spikes Recordable From Axons in Microtunnels. IEEE Trans. Neural Syst. Rehabil. Eng. 22, 453–459. doi:10.1109/TNSRE.2013.2289911.
Pan, L., Alagapan, S., Franca, E., Leondopulos, S. S., DeMarse, T. B., Brewer, G. J., et al. (2015). An in vitro method to manipulate the direction and functional strength between neural populations. Front. Neural Circuits 9, 32. doi:10.3389/fncir.2015.00032.
Pigareva, Y., Gladkov, A., Kolpakov, V., Mukhina, I., Bukatin, A., Kazantsev, V. B., et al. (2021). Experimental Platform to Study Spiking Pattern Propagation in Modular Networks In Vitro. Brain Sci. 11. doi:10.3390/brainsci11060717.
Pimashkin, A., Gladkov, A., Agrba, E., Mukhina, I., and Kazantsev, V. (2016). Selectivity of stimulus induced responses in cultured hippocampal networks on microelectrode arrays. Cogn. Neurodyn. 10, 287–299. doi:10.1007/s11571-016-9380-6.
Pimashkin, A., Gladkov, A., Mukhina, I., and Kazantsev, V. (2013). Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays. Front. Neural Circuits 7, 87. doi:10.3389/fncir.2013.00087.
Potter, S. M., Fraser, S. E., and Pine, J. (1997). Animat in a petri dish: cultured neural networks for studying neural computation. in Proc. 4th Joint Symposium on Neural Computation, UCSD, 167–174.
Reger, B. D., Fleming, K. M., Sanguineti, V., Alford, S., and Mussa-Ivaldi, F. A. (2000). Connecting Brains to Robots: An Artificial Body for Studying the Computational Properties of Neural Tissues. Artif. Life 6, 307–324. doi:10.1162/106454600300103656.
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 Comput. Biol. 4, e1000228. Available at: https://doi.org/10.1371/journal.pcbi.1000228.
Sjöström, P. J., Turrigiano, G. G., and Nelson, S. B. (2001). Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity. Neuron 32, 1149–1164. doi:https://doi.org/10.1016/S0896-6273(01)00542-6.
Song, S., Miller, K. D., and Abbott, L. F. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919. Available at: http://dx.doi.org/10.1038/78829.
Strukov, D. B., Snider, G. S., Stewart, D. R., and Williams, R. S. (2008). The missing memristor found. Nature 453, 80–83. doi:10.1038/nature06932.
Tsodyks, M., Pawelzik, K., and Markram, H. (1998). Neural networks with dynamic synapses. Neural Comput. 10, 821–835.