Skip to main content

Advertisement

SpringerLink
Log in

Spike history neural response model

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

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

    Book  Google Scholar 

  • 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.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

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

    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), 1–11.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  Google Scholar 

  • 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.

    CAS  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

Download references

Acknowledgments

This research was supported by the Australian Research Council (DE120102210). The Bionic Ear Institute acknowledges the support it receives from the Victorian Government through its Operational Infrastructure Support Program. This research was supported by the Australian Research Council through its Special Research Initiative in Bionic Vision Science and Technology grant to Bionic Vision Australia. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.

Conflict of interests

The authors declare that they have no conflict of interest

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia

    Tatiana Kameneva, Daniele Zarelli, Dragan Nĕsić, Anthony N. Burkitt & David B. Grayden

  2. The University of New South Wales, New South Wales, Australia

    Miganoosh Abramian

  3. Bionics Institute, East Melbourne, Australia

    Anthony N. Burkitt & David B. Grayden

  4. Victoria Research Laboratory, National ICT Australia, Melbourne, Australia

    David B. Grayden

  5. National Vision Research Institute, Carlton, Australia

    Hamish Meffin

  6. Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, Australia

    Hamish Meffin

  7. NeuroEngineering Lab, The University of Melbourne, Melbourne, Australia

    Tatiana Kameneva, Anthony N. Burkitt, Hamish Meffin & David B. Grayden

Authors
  1. Tatiana Kameneva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miganoosh Abramian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniele Zarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dragan Nĕsić
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anthony N. Burkitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hamish Meffin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. David B. Grayden
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tatiana Kameneva.

Additional information

Action Editor: Tim Gollisch

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kameneva, T., Abramian, M., Zarelli, D. et al. Spike history neural response model. J Comput Neurosci 38, 463–481 (2015). https://doi.org/10.1007/s10827-015-0549-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-015-0549-5

Keywords

Search

Navigation