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Erschienen in:

01.06.2015

Spike history neural response model

verfasst von: Tatiana Kameneva, Miganoosh Abramian, Daniele Zarelli, Dragan Nĕsić, Anthony N. Burkitt, Hamish Meffin, David B. Grayden

Erschienen in: Journal of Computational Neuroscience | Ausgabe 3/2015

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Abstract

There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.

Vorheriger Artikel Erratum to: Bayes optimal template matching for spike sorting – combining fisher discriminant analysis with optimal filtering

Nächster Artikel Granger causality-based synaptic weights estimation for analyzing neuronal networks

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Abbas, J.J., & Chizeck, H.J. (1991). Feedback control of coronal plane hip angle in paraplegic subjects using functional neuromuscular stimulation. IEEE Transactions Biomedical Engineering, 38, 687–698.CrossRef

Dayan, P., & Abbott, L.F. (2001). Theoretical neuroscience: computational and mathematical modeling of neural systems: MIT press.

Chichilnisky, E.J. (2001). A simple white noise analysis of neuronal light responses. Network: Computation in Neural Systems, 12, 199–213.CrossRef

Clark, G. (2003). Cochlear implants: fundamentals and applications: Springer.

Cooper, H.R., & Craddock, L.C. (Eds.) (2006). Cochlear implants: a practical guide: Wurr Publishers.

Fountas, K.N., Smith, J.R., Murro, A.M., Politsky, J., Park, Y.D., & Jenkins, P.D. (2005). Implantation of a closed-loop stimulation in the management of medically refractory focal epilepsy. Stereotact. Functional Neurosurgery, 83, 153–158.CrossRef

Gerstner, W., & Kistler, W. M. (2002). Spiking neuron models: single neurons, populations, plasticity. Cambridge: Cambridge University Press.CrossRef

Goodwin, G.C., Graebe, S.F., & Salgado, M.E. (2001). Control Systems Design: Prentice Hall International.

Gorzelic, P., Schiff, S.J., & Sinha, A. (2013). Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson’s disease. Journal of Neural Engineering, 026016.

Jepson, L.H., Hottowy P., Matheson K., Gunning, D.E., Dabrowski, W., Litke, A.M., & Chichilnisky, E.J. (2014a). Spatially patterned electrical stimulation to enhance resolution of retinal prostheses. Journal of Neuroscience, 34(14), 4871–4881.CrossRefPubMedCentralPubMed

Jepson, L. H., Hottowy, P., Weiner, G. A., Dabrowski, W., Litke, A. M., & Chichilnisky, E. J. (2014b). High-fidelity reproduction of spatiotemporal visual signals for retinal prosthesis. Neuron, 83, 87–92.CrossRefPubMed

Kalia, S.K., Sankar, T., & Lozano, A.M. (2013). Deep brain stimulation for Parkinson’s disease and other movement disorders. Current Opinion, 26(4), 374–380.CrossRef

Keat, J., Reinagel, P., Reid, R.C., & Meister, M. (2001). Predicting every spike: a model for the responses of visual neurons. Neuron, 30, 803–817.CrossRefPubMed

Koch, C., & Segev, I. (1998). Methods in neuronal modeling: from ions to networks. MIT press.

Lesica, N.A., Jin, J., Weng, C., Yeh, C.I., Butts, D.A., Stanley, G.B., & Alonso, J.M. (2007). Adaptation to stimulus contrast and correlations during natural visual stimulation. Neuron, 55, 479–491.CrossRefPubMedCentralPubMed

Mark, R.E., Jones, B.W., Watt, C.B., & Strettoi, E. (2003). Neural remodeling in retinal degeneration. Progress Retinal Eye Research, 22(5), 607–655.CrossRef

McNeal, D.R. (1976). Analysis of a model for excitation of myelinated nerve. IEEE Transactions on Biomedical Engineering, 23(4), 329–337.CrossRefPubMed

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), 1–11.

Nelson, T.S., Suhr, C.L., Freestone, D.R., Lai, A., Halliday, A.J., McLean, K.J., Burkitt, A.N., & Cook, M.J. (2011). Closed-loop seizure control with very high frequency electrical stimulation at seizure onset in the GEARS model of absence epilepsy. Journal of Neural Systems, 21(2), 163–173.CrossRef

Nirenberg, S., Bornash, I., Pillow, J.W., & Victor J.D. (2010). Heterogeneous response dynamics in retinal ganglion cells: the interplay of predictive coding and adaptation. Journal of Neurophysiology, 103, 3184–3194.CrossRefPubMedCentralPubMed

Tukhlina, N., & Rosenblum, M. (2008). Feedback suppression of neural synchrony in two interacting pPopulations by vanishing stimulation. Journal of Biological Physics, 34, 301– 314.CrossRefPubMedCentralPubMed

Parker, J., Karantonis, D., Single, P., & Obradovic, M. (2012). Compound action potentials recorded in the human spinal cord during neurostimulation for pain relief. Pain, 153(3), 593– 601.CrossRefPubMed

Pillow, J.W., Paninski, L., Uzzell, V.J., Simoncelli, E.P., & Chichilnisky, E.J. (2005). Prediction and decoding of retinal ganglion cell responses with probabilistic spiking model. Journal of Neuroscience, 25(47), 11003–11013.CrossRefPubMed

Pulver, S.R., & Griffith, L.C. (2010). Spike integration and cellular memory in a rhythmic network from Na/K pump current dynamics. Nature Neuroscience, 13, 53–59.CrossRefPubMedCentralPubMed

Rattay, F. (1999). The basic mechanism for the electrical stimulation of the nervous system. Neuroscience, 89 (2), 335–346.CrossRefPubMed

Rose, T., Gras, H., & Horner, M. (2007). Activity-dependent suppression of spontaneous spike generation in the Retzius neurons of the leech Hirudo medicinalis L. Invertebrate Neuroscience, 6(4), 169–176.CrossRef

Rosin, B., Slovik, M., Mitelman, R., Rivlin-Etzion, M., Haber, S.N., Israel, Z., Vaadia, E., & Bergman H. (2011). Closed-loop deep brain stimulation is superior in ameliorating Parkinsonism. Neuron, 72(2), 370–384.CrossRefPubMed

Schiff, S.J. (2009). Kalman meets neuron: the emerging intersection of control theory with neuroscience. Proceedings of the IEEE EMBS conference, 1–4.

Sun, J.J., Kilb, W., & Luhmann, H.J. (2010). Self-organization of repetitive spike patterns in developing neuronal networks in vitro. European Journal of Neuroscience, 1–11.

Stingl, et al (2013). Artificial vision with wirelessly powered subretinal electronic implant alpha-IMS. Proceedings of the Royal Society: Biological Sciences, 280(1757), 20130077.CrossRefPubMedCentral

van Hateren, J.N., Ruttiger, L., Sun, H., & Lee, B.B. (2002). Processing of natural temporal stimuli by macaque retinal ganglion cells. Journal of Neuroscience, 22(22), 9945–9960.PubMed

Victor, J.D., & Purpura, K.P. (1997). Metric-space analysis of spike trains: theory, algorithms and application. Network: Computation in Neural Systems, 8, 127–164.CrossRef

Vetter, A.H., Masani, K., Nakazawa, K., & Popovic, M.R. (2010). Neural-mechanical feedback control scheme generates physiological ankle torque fluctuation during quiet stance IEEE. Transactions on Neural Systems Rehabililation Engineering, 18(1), 86–95.CrossRef

Wallach, A., Eytan, D., Gal, A., Zrenner, C., & Marom, S. (2011). Neuronal response clamp. Frontiers in Neuroengineering, 4, 1–10.CrossRef

Zimmermann, J. B., & Jackson, A. (2014). Closed-loop control of spinal cord stimulation to restore hand function after paralysis. Frontiers in Neuroscience, 10, 3389.

Titel: Spike history neural response model
verfasst von: Tatiana Kameneva
Miganoosh Abramian
Daniele Zarelli
Dragan Nĕsić
Anthony N. Burkitt
Hamish Meffin
David B. Grayden
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 3/2015
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-015-0549-5

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