CyberRat Probes: High-Resolution Biohybrid Devices for Probing the Brain

  • Stefano Vassanelli
  • Florian Felderer
  • Mufti Mahmud
  • Marta Maschietto
  • Stefano Girardi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7375)


Neuronal probes can be defined as biohybrid entities where the probes and nerve cells establish a close physical interaction for communicating in one or both directions. During the last decade neuronal probe technology has seen an exploded development. This paper presents newly developed chip–based CyberRat probes for enhanced signal transmission from nerve cells to chip or from chip to nerve cells with an emphasis on invivo interfacing, either in terms of signal−to−noise ratio or of spatiotemporal resolution. The oxide−insulated chips featuring large−scale and high−resolution arrays of stimulation and recording elements are a promising technology for high spatiotemporal resolution biohybrid devices, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on SigMate, an inhouse comprehensive automated tool for processing and analysis of acquired signals by such large scale biohybrid devices.


Neuronal probe brain recording brain stimulation biohybrid devices neuronal signal analysis 


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  1. 1.
    Rutten, W.L.: Selective electrical interfaces with the nervous system. Annu. Rev. Biomed. Eng. 4, 407–452 (2002)CrossRefGoogle Scholar
  2. 2.
    Fromherz, P.: Neuroelectronic Interfacing: Semiconductor chips with Ion Channels, Nerve cells, and Brain. In: Waser, R. (ed.) Nanoelectronics and Information Technology, pp. 781–810. Wiley–VCH, Berlin (2003)Google Scholar
  3. 3.
    Wise, K.D., et al.: Wireless implantable microsystems: high-density electronic interfaces to the nervous system. Proc. IEEE 92, 76–97 (2004)CrossRefGoogle Scholar
  4. 4.
    Lebedev, M.A., Nicolelis, M.A.: Brain-machine interfaces: past, present and future. Trends. Neurosci. 29(9), 537–546 (2006)CrossRefGoogle Scholar
  5. 5.
    Hochberg, L.R., et al.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099), 164–171 (2006)CrossRefGoogle Scholar
  6. 6.
    Vassanelli, S., Fromherz, P.: Transistor probes local potassium conductances in the adhesion region of cultured rat hippocampal neurons. J. Neurosci. 19(16), 6767–6773 (1999)Google Scholar
  7. 7.
    Vassanelli, S., Fromherz, P.: Transistor records of excitable neurons from rat brain. Appl. Phys. A. 66, 459–463 (1998)CrossRefGoogle Scholar
  8. 8.
    Hai, A., Shappir, J., Spira, M.E.: Long-term, multisite, parallel, in-cell recording and stimulation by an array of extracellular microelectrodes. J. Neurophysiol. 104, 559–568 (2010)CrossRefGoogle Scholar
  9. 9.
    Lambacher, A., et al.: Electrical imaging of neuronal activity by multi–transistor–array (MTA) recording at 7.8 μm resolution. Appl. Phys. A. 79(7), 1607–1611 (2004)CrossRefGoogle Scholar
  10. 10.
    Hutzler, M., et al.: High-resolution multitransistor array recording of electrical field potentials in cultured brain slices. J. Neurophysiol. 96, 1638–1645 (2006)CrossRefGoogle Scholar
  11. 11.
    Girardi, S., Maschietto, M., Zeitler, R., Mahmud, M., Vassanelli, S.: High resolution cortical imaging using electrolyte–(metal)–oxide–semiconductor field effect transistors. In: 5th Intl. IEEE -EMBS Conf. on Neural Eng., pp. 269–272. IEEE Press, New York (2011)CrossRefGoogle Scholar
  12. 12.
    Vassanelli, S., Mahmud, M., Girardi, S., Maschietto, M.: On the Way to Large–Scale and High–Resolution Brain–Chip Interfacing. Cogn. Comput. 4(1), 71–81 (2012)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Berdondini, L., et al.: A microelectrode array (MEA) integrated with clustering structures for investigating in vitro neurodynamics in confined interconnected subpopulations of neurons. Sens. Actuat. B: Chem. 114, 530–541 (2006)CrossRefGoogle Scholar
  15. 15.
    Berdondini, L., et al.: Extracellular recordings from high density microelectrode arrays coupled to dissociated cortical neuronal cultures. J. Neurosci. Meth. 177, 386–396 (2009)CrossRefGoogle Scholar
  16. 16.
    Potter, S.M., Wagenaar, D.A., DeMarse, T.B.: Closing the loop: stimulation feedback systems for embodied MEA cultures. In: Taketani, M., Baudry, M. (eds.) Advances in Network Electrophysiology: Using Multi–Electrodes–Arrays, pp. 215–242. Springer, New York (2005)Google Scholar
  17. 17.
    Fromherz, P.: Joining ionics and electronics: semiconductor chips with ion channels, nerve cells, and brain tissue. In: 2005 IEEE International Solid–State Circuits Conference (Tech. Dig. ISSCC), pp. 76–77. IEEE Press, New York (2005)CrossRefGoogle Scholar
  18. 18.
    Stangl, C., Fromherz, P.: Neuronal field potential in acute hippocampus slice recorded with transistor and micropipette electrode. Eur. J. Neurosci. 27, 958–964 (2008)CrossRefGoogle Scholar
  19. 19.
    Imfeld, K., et al.: Large–scale, high–resolution data acquisition system for extracellular recording of electrophysiological activity. IEEE T. Bio.–Med. Eng. 55(8), 2064–2072 (2008)CrossRefGoogle Scholar
  20. 20.
    Frey, U., et al.: Switch–matrix–based high–density microelectrode array in CMOS technology. IEEE J. Solid–St. Circ. 45(2), 467–482 (2010)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Eversmann, B., et al.: A 128 × 128 CMOS biosensor array for extracellular recording of neural activity. IEEE J. Solid–St. Circ. 38(12), 2306–2317 (2003)CrossRefGoogle Scholar
  22. 22.
    Eversmann, B., Lambacher, A., Gerling, G., Kunze, A.: A neural tissue interfacing chip for in–vitro applications with 32 k recording / stimulation channels on an active area of 2.6 mm2. In: 37th Solid–State Circuits Conference (ESSCIRC), pp. 211–214. IEEE Press, New York (2011)Google Scholar
  23. 23.
    Jones, K.E., Campbell, P.K., Normann, R.A.: A glass/silicon composite intracortical electrode array. Ann. Biomed. Eng. 20, 423–437 (1992)CrossRefGoogle Scholar
  24. 24.
    Kipke, D.R., Vetter, R.J., Williams, J.C., Hetke, J.F.: Silicon–substrate intracortical microelectrode arrays for long–term recording of neuronal spike activity in cerebral cortex. IEEE T. Neur. Sys. Reh. 11(2), 151–155 (2003)CrossRefGoogle Scholar
  25. 25.
    Lee, J., Rhew, H.G., Kipke, D.R., Flynn, M.P.: A 64 Channel Programmable Closed–Loop Neurostimulator With 8 Channel Neural Amplifier and Logarithmic ADC. IEEE J. Solid–St. Circ. 45(9), 1935–1945 (2010)CrossRefGoogle Scholar
  26. 26.
    Azin, M., Guggenmos, D.J., Barbay, S., Nudo, R.J., Mohseni, P.: A BatteryPowered Activity–Dependent Intracortical Microstimulation IC for Brain–Machine–Brain Interface. IEEE J. Solid–St. Circ. 46(4), 731–745 (2011)CrossRefGoogle Scholar
  27. 27.
    Venkatraman, S., et al.: In Vitro and In Vivo Evaluation of PEDOT Microelectrodes for Neural Stimulation and Recording. IEEE T. Neur. Sys. Reh. 19(3), 307–316 (2011)CrossRefGoogle Scholar
  28. 28.
    Buzsaki, G.: Large–scale recording of neuronal ensembles. Nat. Neurosci. 7(5), 446–451 (2004)CrossRefGoogle Scholar
  29. 29.
    Prochazka, A., Mushahwar, V.K., McCreery, D.: Neuralprostheses. J. Physiol. 533(pt. 1), 99–109 (2001)Google Scholar
  30. 30.
    Quiroga, R.Q., Nadasdy, Z., Ben–Shaul, Y.: Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16(8), 1661–1687 (2004)zbMATHCrossRefGoogle Scholar
  31. 31.
    Kwon, K.Y., Eldawlatly, S., Oweiss, K.G.: NeuroQuest: A comprehensive analysis tool for extracellular neural ensemble recordings. J. Neurosci. Meth. 204(1), 189–201 (2012)CrossRefGoogle Scholar
  32. 32.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single–trial EEG dynamics including independent component analysis. J. Neurosci. Meth. 134(1), 9–21 (2004)CrossRefGoogle Scholar
  33. 33.
    Bokil, H.S., Andrews, P., Kulkarni, J.E., Mehta, S., Mitra, P.P.: Chronux: A platform for analyzing neural signals. J. Neurosci. Meth. 192, 146–151 (2010)CrossRefGoogle Scholar
  34. 34.
    Cui, J., Xu, L., Bressler, S.L., Ding, M., Liang, H.: BSMART: A Matlab/C toolbox for analysis of multichannel neural time series. Neural Net. 21(8), 1094–1104 (2008)CrossRefGoogle Scholar
  35. 35.
    Egert, U., et al.: MEA–Tools: an open source toolbox for the analysis of multi–electrode data with Matlab. J. Neurosci. Meth. 117(1), 33–42 (2002)CrossRefGoogle Scholar
  36. 36.
    Gunay, C., et al.: Database analysis of simulated and recorded electrophysiological datasets with PANDORAs toolbox. Neuroinformatics 7(2), 93–111 (2009)CrossRefGoogle Scholar
  37. 37.
    Huang, Y., et al.: An integrative analysis platform for multiple neural spike train data. J. Neurosci. Meth. 172(2), 303–311 (2008)CrossRefGoogle Scholar
  38. 38.
    Magri, C., Whittingstall, K., Singh, V., Logothetis, N., Panzeri, S.: A toolbox for the fast information analysis of multiple–site LFP, EEG and spike train recordings. BMC Neurosci. 10(1), 81 (2009)CrossRefGoogle Scholar
  39. 39.
    Vato, A., et al.: Spike manager: a new tool for spontaneous and evoked neuronal networks activity characterization. Neurocomputing 58-60, 1153–1161 (2004)CrossRefGoogle Scholar
  40. 40.
    Versace, M., Ames, H., Lveill, J., Fortenberry, B., Gorchetchnikov, A.: KInNeSS: a modular framework for computational neuroscience. Neuroinf. 6(4), 291–309 (2008)CrossRefGoogle Scholar
  41. 41.
    Lidierth, M.: sigTOOL:A Matlab-based environment for sharing laboratory developed software to analyze biological signals. J. Neurosci. Meth. 178, 188–196 (2009)CrossRefGoogle Scholar
  42. 42.
    Meier, R., Egert, U., Aertsen, A., Nawrot, M.P.: FIND–A unified framework for neural data analysis. Neural Networks 21(8), 1085–1093 (2008)CrossRefGoogle Scholar
  43. 43.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: SigMate: A MATLAB–based automated tool for extracellular neuronal signal processing and analysis. J. Nerusci. Meth. 207, 97–112 (2012)CrossRefGoogle Scholar
  44. 44.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: SigMate: a Matlab–based neuronal signal processing tool. In: 32nd Intl. Conf. of IEEE EMBS, pp. 1352–1355. IEEE Press, New York (2010)Google Scholar
  45. 45.
    Mahmud, M., et al.: SigMate: A Comprehensive Software Package for Extracellular Neuronal Signal Processing and Analysis. In: 5th Intl. Conf. on Neural Eng., pp. 88–91. IEEE Press, New York (2011)CrossRefGoogle Scholar
  46. 46.
    Weis, R., Muller, B., Fromherz, P.: Neuron adhesion on a silicon chip probed by an array of field–effect transistors. Phys. Rev. Lett. 76(2), 327–330 (1996)CrossRefGoogle Scholar
  47. 47.
    Schmidtner, M., Fromherz, P.: Functional Na+ channels in cell adhesion probed by transistor recording. Biophys. J. 90, 183–189 (2006)CrossRefGoogle Scholar
  48. 48.
    Lambacher, A., et al.: Identifying Firing Mammalian Neurons in Networks with High–Resolution Multi–Transistor Array (MTA). Appl. Phys. A. 102, 1–11 (2011)CrossRefGoogle Scholar
  49. 49.
    Felderer, F., Fromherz, P.: Transistor needle chip for recording in brain tissue. App. Phys. A. 104, 1–6 (2011)CrossRefGoogle Scholar
  50. 50.
    Swanson, L.W.: Brain Maps: Structure of the Rat Brain. Academic, London (2003)Google Scholar
  51. 51.
    Mahmud, M., Girardi, S., Maschietto, M., Pasqualotto, E., Vassanelli, S.: An automated method to determine angular preferentiality using LFPs recorded from rat barrel cortex by brain–chip interface under mechanical whisker stimulation. In: 33rd Intl. Conf. of IEEE EMBS, pp. 2307–2310. IEEE Press, New York (2011)Google Scholar
  52. 52.
    Maschietto, M., et al.: Local field potentials recording from the rat brain cortex with transistor needle chips (unpublished)Google Scholar
  53. 53.
    Hofstotter, C., et al.: The Cerebellum chip: an analog VLSI Implementation of a Cerebellar Model of Classical Conditioning. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, pp. 577–584. MIT Press, Cambridge (2005)Google Scholar
  54. 54.
    Hofstotter, C., Mintz, M., Verschure, P.F.M.J.: The cerebellum in action: a simulation and robotics study. Eur. J. Neurosci. 16, 1361–1376 (2002)CrossRefGoogle Scholar
  55. 55.
    Vershure, P.F.M.J., Mintz, M.: A real–time model of the cerebellar circuitry underlying classical conditioning: A combined simulation and robotics study. Neurocomputing 38-40, 1019–1024 (2001)CrossRefGoogle Scholar
  56. 56.
    Liu, S.C., Delbruck, T.: Neuromorphic sensory systems. Curr. Opin. Neurobiol. 20(2), 288–295 (2010)CrossRefGoogle Scholar
  57. 57.
    Wen, B., Boahen, K.: A silicon cochlea with active coupling. IEEE T. Biomed. Circ. S. 3(6), 444–455 (2009)CrossRefGoogle Scholar
  58. 58.
    Heming, E.A., Choo, R., Davies, J.N., Kiss, Z.H.T.: Designing a thalamic somatosensory neural prosthesis: Consistency and persistence of percepts evoked by electrical stimulation. IEEE T. Neur. Sys. Reh. 19(5), 477–482 (2011)CrossRefGoogle Scholar
  59. 59.
    Ahrens, K.F., Kleinfeld, D.: Current flow in vibrissa motor cortex can phase-lock with exploratory rhythmic whisking in rat. J. Neurophysiol. 92, 1700–1707 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stefano Vassanelli
    • 1
  • Florian Felderer
    • 2
  • Mufti Mahmud
    • 1
    • 3
  • Marta Maschietto
    • 1
  • Stefano Girardi
    • 1
  1. 1.NeuroChip LabUniversity of PadovaPadovaItaly
  2. 2.Max Planck Institute of BiochemistryMartinsriedGermany
  3. 3.Institute of Information TechnologyJahangirnagar UniversitySavarBangladesh

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