Abstract
Real-time prediction of dynamical characteristics of Dopamine (DA) neurons, including properties in ion channels and membrane potentials, is meaningful and critical for the investigation of the dynamical mechanisms of DA cells and the related psychiatric disorders. However, obtaining the unobserved states of DA neurons is significantly challenging. In this paper, we present a real-time prediction system for DA unobserved states on a reconfigurable field-programmable gate array (FPGA). In the presented system, the unscented Kalman filter (UKF) is implemented into a DA neuron model for dynamics prediction. We present a modular structure to implement the prediction algorithm and a digital topology to compute the roots of matrices in the UKF implementation. Implementation results show that the proposed system provides the real-time computational ability to predict the DA unobserved states with high precision. Although the presented system is aimed at the state prediction of DA cells, it can also be applied into the dynamic-clamping technique in the electrophysiological experiments, the brain-machine interfaces and the neural control engineering works.
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References
Wise, R.A.: Dopamine, learning and motivation. Nat. Rev. Neurosci. 5(6), 483–494 (2004)
Braver, T.S., Barch, D.M., Cohen, J.D.: Cognition and control in Schizophrenia: a computational model of dopamine and prefrontal function. Biol. Psychiatry 46(3), 312–328 (1999)
Iversen, S.D., Iversen, L.L.: Dopamine: 50 years in perspective. Trends Neurosci. 30(5), 188–193 (2007)
Schultz, W.: Behavioral dopamine signals. Trends Neurosci. 30(5), 203–210 (2007)
Grace, A.A., Bunney, B.S.: The control of firing pattern in nigral dopamine neurons: burst firing. J. Neurosci. 4(11), 2877–2890 (1984)
Hyland, B.I., Reynolds, J.N.J., Hay, J., Perk, C.G., Miller, R.: Firing modes of midbrain dopamine cells in the freely moving rat. Neuroscience 114(2), 475–492 (2002)
Grace, A.A.: Phasic versus tonic dopamine release and the modulation of dopamine system responsivity: a hypothesis for the etiology of Schizophrenia. Neuroscience 41(1), 1–24 (1991)
Nestler, E.J., Carlezon, W.A.: The mesolimbic dopamine reward circuit in depression. Biol. Psychiatry 59(12), 1151–1159 (2006)
Morozova, E.O., Zakharov, D., Gutkin, B.S., Lapish, C.C., Kuznetsov, A.: Dopamine neurons change the type of excitability in response to stimuli. PLoS Comput. Biol. 12(12), e1005233 (2016)
Guzman, J.N., Sánchez-Padilla, J., Chan, C.S., Surmeier, D.J.: Robust pacemaking in substantia nigra dopaminergic neurons. J. Neurosci. 29(35), 11011–11019 (2009)
Khaliq, Z.M., Bean, B.P.: Pacemaking in dopaminergic ventral tegmental area neurons: depolarizing drive from background and voltage-dependent sodium conductances. J. Neurosci. 30(21), 7401–7413 (2010)
Yang, S., Deng, B., Wang, J., Li, H., Liu, C., Fietkiewicz, C., Loparo, K.A.: Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties. Sci. Rep. 7, 40152 (2017)
Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)
Yang, S., Wang, J., Li, S., Deng, B., Wei, X., Yu, H., Li, H.: Cost-efficient FPGA implementation of basal ganglia and their Parkinsonian analysis. Neural Netw. 71, 62–75 (2015)
Yang, S., Wang, J., Li, S., Li, H., Wei, X., Yu, H., Deng, B.: Digital implementations of thalamocortical neuron models and its application in thalamocortical control using FPGA for Parkinson׳s disease. Neurocomputing 177, 274–289 (2016)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61374182, in part by the National Natural Science Foundation of China under Grant 61601331, and in part by the National Natural Science Foundation of China under Grant 61471265.
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Yang, S. et al. (2017). Real-Time Prediction of the Unobserved States in Dopamine Neurons on a Reconfigurable FPGA Platform. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_72
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DOI: https://doi.org/10.1007/978-3-319-70093-9_72
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