Advertisement

Uncovering the Neural Code Using a Rat Model during a Learning Control Task

  • Chenhui Yang
  • Hongwei Mao
  • Yuan Yuan
  • Bing Cheng
  • Jennie Si
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7311)

Abstract

How neuronal firing activities encode meaningful behavior is an ultimate challenge to neuroscientists. To make the problem tractable, we use a rat model to elucidate how an ensemble of single neuron firing events leads to conscious, goal-directed movement and control. This study discusses findings based on single unit, multi-channel simultaneous recordings from rats frontal areas while they learned to perform a decision and control task. To study neural firing activities, first and foremost we needed to identify single unit firing action potentials, or perform spike sorting prior to any analysis on the ensemble of neural activities. After that, we studied cortical neural firing rates to characterize their changes as rats learned a directional paddle control task. Single units from the rat’s frontal areas were inspected for their possible encoding mechanism of directional and sequential movement parameters. Our results entail both high level statistical snapshots of the neural data and more detailed neuronal roles in relation to rat’s learning control behavior.

Keywords

Control Task Primary Motor Cortex Neural Code Fano Factor Neural Recording 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andersen, R., Musallam, S., Pesaran, B.: Selecting the signals for a brain-machine interface. Current Opinion in Neurobiology 14, 720–726 (2004)CrossRefGoogle Scholar
  2. Bao, P., Zhang, L.: Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Transactions on Medical Imaging 22(9), 1089–1099 (2003)CrossRefGoogle Scholar
  3. Benitez, R., Nenadic, Z.: Robust Unsupervised Detection of Action Potentials With Probabilistic Models. IEEE Transactions on Biomedical Engineering 55(4), 1344–1354 (2008)CrossRefGoogle Scholar
  4. Ben-Shaul, Y., Drori, R., Asher, I., Stark, E., Nadasdy, Z., Abeles, M.: Neuronal activity in motor cortical areas reflects the sequential context of movement. J. Neurophysiol. 91, 1726–1748 (2004)CrossRefGoogle Scholar
  5. Carpenter, A., Georgopoulos, A., Pellizzer, G.: Motor cortical encoding of serial order in a context-recall task. Science 283, 1752–1757 (1999)CrossRefGoogle Scholar
  6. Dedual, N., Ozturk, M., Sanchez, J., Principe, J.: An associative memory readout in ESN for neural action potential detection. In: International Joint Conference on Neural Networks, IJCNN 2007, p. 2295 (2007)Google Scholar
  7. Fee, M.S., Mitra, P.P., Kleinfeld, D.: Variability of extracellular spike waveforms of cortical neurons. Journal of Neurophysiology 76(6), 3823–3833 (1996)Google Scholar
  8. Hulata, E., Segev, R., Ben-Jacob, E.: A method for spike sorting and detection based on wavelet packets and shannon’s mutual information. Journal of Neuroscience Methods 117(1), 1–12 (2002)CrossRefGoogle Scholar
  9. Hulata, E., Segev, R., Shapira, Y., Benveniste, M., Ben-Jacob, E.: Detection and sorting of neural spikes using wavelet packets. Phys. Rev. Lett. 85(21), 4637–4640 (2000)CrossRefGoogle Scholar
  10. Humphrey, D., Schmidt, E.: Extracellular Single-Unit Recording Methods. Neurophysiological Techniques: Applications to Neural Systems 15, 1–64 (1991)CrossRefGoogle Scholar
  11. Kakei, S., Hoffman, D., Strick, P.: Muscle and movement representations in the primary motor cortex. Science 285, 2136–2139 (1999)CrossRefGoogle Scholar
  12. Kandel, E., Schwartz, J., Jessell, T.: Principles of Neural Science (2000)Google Scholar
  13. Kargo, W., Nitz, D.: Improvements in the Signal-to-Noise Ratio of Motor Cortex Cells Distinguish Early versus Late Phases of Motor Skill Learning. J. Neurosci. 24, 5560–5569 (2004)CrossRefGoogle Scholar
  14. Kim, K.H., Kim, S.J.: A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio. IEEE Transactions on Biomedical Engineering 50(8), 999–1011 (2003)CrossRefGoogle Scholar
  15. Kreiter, A.K., Aertsen, A.M., Gerstein, G.L.: A low-cost single-board solution for real-time, unsupervised waveform classification of multineuron recordings. Journal of Neuroscience Methods 30(1), 59–69 (1989)CrossRefGoogle Scholar
  16. Li, C., Padoa-Schioppa, C., Bizzi, E.: Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron 30, 593–607 (2001)CrossRefGoogle Scholar
  17. Lu, X., Ashe, J.: Anticipatory activity in primary motor cortex codes memorized movement sequences. Neuron 45, 967–973 (2005)CrossRefGoogle Scholar
  18. Mallat, S.: A wavelet tour of signal processing. Academic Press, San Diego (1989)zbMATHGoogle Scholar
  19. Matsuzaka, Y., Picard, N., Strick, P.: Skill Representation in the Primary Motor Cortex After Long-Term Practice. J. Neurophysiol. 97, 1819–1832 (2007)CrossRefGoogle Scholar
  20. Musial, P., Baker, S., Gerstein, G., King, E., Keating, J.: Signal-to-noise ratio improvement in multiple electrode recording. Journal of Neuroscience Methods 115, 29–43 (2002)CrossRefGoogle Scholar
  21. Naundorf, B., Wolf, F., Volgushev, M.: Unique features of action potential initiation in cortical neurons. Nature 440(7087), 1060–1063 (2006)CrossRefGoogle Scholar
  22. Nenadic, Z.: Spike detection with the continuous wavelet transform, matlab software. University of California, Irvine, Center for BioMedical Signal Processing and Computation (2005), http://cbmspc.eng.uci.edu
  23. Nenadic, Z., Burdick, J.: Spike detection using the continuous wavelet transform. IEEE Transactions on Biomedical Engineering 52(1), 74–87 (2005)CrossRefGoogle Scholar
  24. Nenadic, Z., Burdick, J.: A control algorithm for autonomous optimization of extracellular recordings. IEEE Transactions on Biomedical Engineering 53(5), 941–955 (2006)CrossRefGoogle Scholar
  25. Olson, B., Si, J., Hu, J., He, J.: Closed-loop cortical control of direction using support vector machines. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13(1), 72–80 (2005)CrossRefGoogle Scholar
  26. Oweiss, K., Anderson, D.: A multiresolution generalized maximum likelihood approach for the detection of unknown transient multichannel signals in colored noise with unknown covariance. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2002, vol. 3, pp. 2993–2996 (2002)Google Scholar
  27. Oweiss, K., Anderson, D.: A unified framework for advancing array signal processing technology of multichannel microprobe neural recording devices. In: 2nd Annual International IEEE-EMB Special Topic Conference on Microtechnologies in Medicine and Biology, pp. 245–250 (2002)Google Scholar
  28. Paxinos, G., Watson, C.: The Rat Brain in Stereotaxic Coordinates (2007)Google Scholar
  29. Quiroga, R.Q., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Computation 16(8), 1661–1687 (2004)CrossRefzbMATHGoogle Scholar
  30. Sadler, B.M., Swami, A.: Analysis of multiscale products for step detection and estimation. IEEE Transactions on Information Theory 45(3), 1043–1051 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  31. Santaniello, S., Fiengo, G., Glielmo, L., Catapano, G.: A biophysically inspired microelectrode recording-based model for the subthalamic nucleus activity in parkinson’s disease. Biomedical Signal Processing and Control 3(3), 203–211 (2008)CrossRefGoogle Scholar
  32. Shima, K., Tanji, J.: Neuronal activity in the supplementary and presupplementary motor areas for temporal organization of multiple movements. J. Neurophysiol. 84, 2148–2160 (2000)Google Scholar
  33. Smith, L.: Noisy spike generator, matlab software. University of Stirling, Department of Computing Science and Mathematics (2006), http://www.cs.stir.ac.uk/~lss/noisyspikes/
  34. Song, M.J., Wang, H.: A spike sorting framework using nonparametric detection and incremental clustering. Neurocomputing 69(10-12), 1380 (2006)CrossRefGoogle Scholar
  35. Thakur, P.H., Lu, H., Hsiao, S.S., Johnson, K.O.: Automated optimal detection and classification of neural action potentials in extra-cellular recordings. Journal of Neuroscience Methods 162(1-2), 364–376 (2007)CrossRefGoogle Scholar
  36. Volgushev, M., Malyshev, A., Balaban, P., Chistiakova, M., Volgushev, S., Wolf, F.: Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference. PLoS ONE 3(4), e1962 (2008)CrossRefGoogle Scholar
  37. Wood, F., Black, M., Vargas-Irwin, C., Fellows, M., Donoghue, J.: On the variability of manual spike sorting. IEEE Transactions on Biomedical Engineering 51(6), 912–918 (2004)CrossRefGoogle Scholar
  38. Wu, G., Hallin, R.G., Ekedahl, R.: Multiple action potential waveforms of single units in man as signs of variability in conductivity of their myelinated fibres. Brain Research 742(1-2), 225–238 (1996)CrossRefGoogle Scholar
  39. Xu, Y., Weaver, J., Healy, D., Lu, J.: Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Transactions on Image Processing 3(6), 747–758 (1994)CrossRefGoogle Scholar
  40. Yang, C., Olson, B., Si, J.: A multiscale correlation of wavelet coefficients approach to spike detection. Neural Computation 23, 215–250 (2011)CrossRefzbMATHGoogle Scholar
  41. Yang, X., Shamma, S.: A totally automated system for the detection and classification of neural spikes. IEEE Transactions on Biomedical Engineering 35(10), 806–816 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chenhui Yang
    • 1
  • Hongwei Mao
    • 1
  • Yuan Yuan
    • 1
  • Bing Cheng
    • 1
  • Jennie Si
    • 1
  1. 1.Arizona State UniversityTempeUSA

Personalised recommendations