Abstract
Hardware-based Artificial Intelligence (A.I.) has many potential applications in biomedical technology; for example, connecting expert systems to biosensors for real-time physiological monitoring of a range of biomarkers, or interfacing with the brain.We suggest that memristors are well-placed to interface directly with neurons to interconnect between computer hardware and the brain for 3 reasons: memristors are widely-touted as neuromorphic computing elements; memristor theory has been successfully used to model spike-time dependent plasticity and the Hodgkin-Huxley model of the neuron; and, the d.c. response of the memristor is a current spike. In this chapter we show that connecting a spiking memristor network to electrically active neuronal cells causes a change in the memristor network dynamics by: removing the memristor spikes, which we show is due to the effects of connection to aqueous medium; causing a change in current decay rate consistent with a change in memristor state; presenting more-linear Iāāāt dynamics; and increasing the memristor spiking rate, as a consequence of interaction with the active cells. This demonstrates that such cells are capable of communicating directly with memristors, without the need for computer translation.
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References
Abdullah, H., Cosby, S.L., Heaney, L., McGarvey, L.: S123 the effect of rhinovirus infection on cough receptors on human sensory nerve and human primary bronchial epithelial cells. Thorax 66, A57 (2011)
Benninger, D.H., Lomarev, M., Lopez, G., Wassermann, E.M., Li, X., Considine, E., Hallett, M.: Transcranial direct current stimulation for the treatment of Parkinsonās disease. Journal of Neurology, Neurosurgery & PsychiatryĀ 81(10), 1105ā1111 (2010)
Bull, L., Uroukov, I.S.: Initial results from the use of learning classifier systems to control in vitro neuronal networks. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 369ā376. ACM (2007)
Carbone, E., Sher, E., Clementi, F.: Ca currents in human neuroblastoma IMR32 cells: kinetics, permeability and pharmacology. Pflugers Archiv. 416(1-2), 170ā179 (1990)
Caporale, N., Dan, Y.: Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci.Ā 31, 25ā46 (2008)
ChabardĆ©s, S., Kahane, P., Minotti, L., Koudsie, A., Hirsch, E., Benabid, A.L.: Deep brain stimulation in epilepsy with particular reference to the subthalamic nucleus. Epileptic Disorders: International Epilepsy Journal with VideotapeĀ 4, S83āS93 (2002)
Chua, L.O.: Memristor - the missing circuit element. IEEE Trans. Circuit TheoryĀ 18, 507ā519 (1971)
Chua, L., Sbitnev, V., Kim, H.: Hodgkin-huxley axon is made of memristors. International Journal of Bifurcation and ChaosĀ 22, 1230011 (2012)
Chua, L., Sbitnev, V., Kim, H.: Neurons are poised near the edge of chaos. International Journal of Bifurcation and ChaosĀ 11, 1250098 (2012)
Coiera, E.: The Guide to Health Informatics, 2nd edn. CRC Press (2003)
Ebong, I., Mazumder, P.: Memristor based STDP learning network for position detection. In: Proceesings of 2010 International Conference on Microelectronics (ICM), pp. 292ā295 (2010), doi:10.1109/ICM.2010.5696142
Erokhin, V., Berzina, T., Camorani, P., Smerieri, A., Vavoulis, D., Feng, J., Fontana, M.P.: Material memristive device circuits with synaptic plasticity: learning and memory. BioNanoScience 1(1-2), 24ā30 (2011)
Gale, E., de Lacy Costello, B., Adamatzky, A.: Memristor-based information gathering approaches, both ant-inspired and hypothetical. Nano Communication NetworksĀ 3(4), 203ā216 (2012)
Gale, E.M., de Lacy Costello, B., Adamatzky, A.: Observation and characterization of memristor current spikes and their application to neuromorphic computation. In: 2012 International Conference on Numerical Analysis and Applied Mathematics (ICNAAM 2012), Kos, Greece (September 2012)
Gale, E., de Lacy Costello, B., Adamatzky, A.: Observation, characterisation and modeling of memristor current spikes. Appl. Math. Inf. Sci.Ā 7, 1395ā1403 (2013)
Gale, E., de Lacy Costello, B., Adamatzky, A.: Boolean logic gates from a single memristor via low-level sequential logic. In: Mauri, G., Dennunzio, A., Manzoni, L., Porreca, A.E. (eds.) UCNC 2013. LNCS, vol.Ā 7956, pp. 79ā89. Springer, Heidelberg (2013), doi:10.1007/978-3-642-39074-6_9
Gale, E., Mayne, R., Adamatzky, A., de Lacy Costello, B.: Drop-coated titanium dioxide memristors. Materials Chemistry and PhysicsĀ 143, 524ā529 (2014)
Georgilas, I., Gale, E., Adamatzky, A., Melhuish, C.: UAV Horizon Tracking Using Memristors and Cellular Automata Visual Processing. In: Natraj, A., Cameron, S., Melhuish, C., Witkowski, M. (eds.) TAROS 2013. LNCS, vol.Ā 8069, pp. 64ā75. Springer, Heidelberg (2014)
Gale, E., Matthews, O., de Lacy Costello, B., Adamatzky, A.: Beyond markov chains, towards adaptive memristors network-based music generation. International Journal of Unconventional Computing (2014)
Gale, E., de Lacy Costello, B., Adamatzky, A.: Emergent spiking in non-ideal memristor networks. Microelectronics Journal (2014), doi:10.1016/j.mejo.2014.06.008
Gale, E., Pearson, D., Kitson, S., Adamatzky, A., de Lacy Costello, B.: The effect of changing electrode material on solution-processed flexible titanium dioxide memrsitors. Materials Chemistry and Physics (2014) (Forthcoming)
Gergel-Hackett, N., Hamadani, B., Dunlap, B., Suehle, J., Richter, C., Hacker, C., Gundlach, D.: A flexible solution-processed memristor. IEEE Electron Device LettersĀ 30(7), 706ā708 (2009)
Hebb, D.O.: The organization of behavior: A neuropsychological theory. Psychology Press (2002)
Hodgkin, A.L., Huxley, A.F.: Propagation of electrical signals along giant nerve fibres. In: Proceedings of the Royal Society of London. Series B, Biological Sciences, pp. 177ā183 (1952)
Howard, G., Gale, E., Bull, L., de Lacy Costello, B., Adamatzky, A.: Evolution of Plastic Learning in Spiking Networks via Memristive Connections. IEEE Transactions on Evolutionary Computation 16(5), 711ā729 (2012)
Howard, G., Bull, L., de Lacy Costello, B., Gale, E., Adamatzky, A.: Evolution of Plastic Learning in Spiking Networks via Memristive Connections. Evolutionary computation 22(1), 79ā103 (2014)
Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P., Lu, W.: Nanoscale memristor device as synapse in neuromorphic systems. Nano Letters 10(4), 1297ā1301 (2010), doi:10.1021/nl904092h
Kim, H., Sah, M.P., Yang, C., Roska, T., Chua, L.O.: Memristor bridge synapses. Proceedings of the IEEE 100(6), 2061ā2070 (2012), doi:10.1109/JPROC.2011.2166749
Linares-Barranco, B., Serrano-Gotarredona, T.: Memristance can explain spike-time-dependent-plasticity in neural synapses. Nature Precedings, 1ā4 (2009)
Miranda, E.R., Bull, L., Gueguen, F., Uroukov, I.S.: Computer Music Meets Unconventional Computing: Towards Sound Synthesis with in vitro neuronal network. Computer MusicĀ 33, 9ā18 (2009)
Patra, P.S.K., Sahu, D.P., Mandal, I.: An expert system for diagnosis of human diseases. Int. J. Comput. Appl. 13(1), 71ā73 (2010)
Prodromakis, T., Chua, L.: Two centuries of memristors. Nature MaterialsĀ 11, 478ā481 (2012)
Querlioz, D., Bichler, O., Gamrat, C.: Simulation of a memristor-based spiking neural network immune to device variations. In: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN). IEEE (2011)
Shortliffe, E.H.: Medical Expert Systems Knowledge Tools for Physicians. West. J. Med. 145(6), 830ā839 (1986)
Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. NatureĀ 453, 80ā83 (2008)
Szolovits, P., Patil, R.S., Schwartz, W.B.: Artificial Intelligence in Medical Diagnosis. Annals of Internal Medicine 108(1), 80ā87 (1988)
Tumilowicz, J.J., Nichols, W.W., Cholon, J.J., Greene, A.E.: Definition of a continuous human cell line derived from neuroblastoma. Cancer ResearchĀ 30(8), 2110ā2118 (1970)
Uroukov, I.S., Ma, M., Bull, L., Purcell, W.M.: Electrophysical measurements in three-dimensional in vivo-mimetic organotypic cell cultures: Preliminary studies with hen embriyo brain spheroids. Neuroscience LettersĀ 404, 33ā38 (2006)
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Gale, E., Iqbal, A., Davey, J., Gater, D. (2015). Neural Net to Neuronal Network Memristor Interconnects. In: Pancerz, K., Zaitseva, E. (eds) Computational Intelligence, Medicine and Biology. Studies in Computational Intelligence, vol 600. Springer, Cham. https://doi.org/10.1007/978-3-319-16844-9_8
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DOI: https://doi.org/10.1007/978-3-319-16844-9_8
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