Skip to main content
Log in

Neuronal spatial learning

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Neurons are electrically active structures determined by the evolution of ion-specific pumps and channels that allow the transfer of charges under the influence of electric fields and concentration gradients. Extensive studies of spike timing of neurons and the relationship to learning exist. However, the properties of spatial activations during action potential in the context of learning have to our knowledge not been consistently studied. We examined spatial propagation of electrical signal for many consecutive spikes using recorded information from tetrodes in freely behaving rats before and during rewarded T-maze learning tasks. Analyzing spatial spike propagation in expert medium spiny neurons with the charge movement model we show that electrical flow has directionality which becomes organized with behavioral learning. This implies that neurons within a network may behave as “weak learners” attending to preferred spatial directions in the probably approximately correct sense. Importantly, the organization of spatial electrical activity within the neuronal network could be interpreted as representing a change in spatial activation of neuronal ensemble termed “strong learning.” Together, the subtle yet critical modulations of electrical flow directivity during weak and strong learning represent the dynamics of what happens in the neuronal network during acquisition of a behavioral task.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Aiello G.L., Bach-y-Rita P. (2000): The cost of an action potential. Journal of Neuroscience Methods 103(2): 145–149

    Article  Google Scholar 

  2. Apicella P. (2002): Tonically active neurons in the primate striatum and their role in the processing of information about motivationally relevant events. European Journal Neuroscience 16(11): 2017–2026

    Article  Google Scholar 

  3. Atukorale A.S., Downs T., Suganthan P.N. (2003): Boosting the HONG network. Neurocomputing 51, 75–86

    Article  Google Scholar 

  4. Aur D., Connolly C.I., Jog M.S. (2005): Computing spike directivity with tetrodes. Journal of Neuroscience Methods 149(1): 57–63

    Article  Google Scholar 

  5. Aur D., Connolly C.I., Jog M.S. (2006): Computing information in neuronal spikes. Neural Processing Letters 23, 183–199

    Article  Google Scholar 

  6. Aur D., Jog M.S. (2006): Building spike representation in tetrodes. Journal of Neuroscience Methods 157(2): 364–373

    Article  Google Scholar 

  7. Barnes, T. D., Kubota, Y., Hu, D., Jin, D. Z. and Graybiel, A. M.: Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories, Nature 437(7062) (2005), 1158–1161.

    Google Scholar 

  8. Beierlein M., Gibson J.R., Connors B.W. (2000): A network of electrically coupled interneurons drives synchronized inhibition in neocortex. Nature Neuroscience 3, 904–910

    Article  Google Scholar 

  9. Bokil H., Laaris N., Blinder K., Ennis M., Keller A. (2001): Ephaptic interactions in the mammalian olfactory system. The Journal of Neuroscience 21(173): 1–5

    Google Scholar 

  10. Bowman A.W., Azzalini A. (1997). Applied smoothing techniques for data analysis. oxford statistical science series, 18. Clarendon Press, Oxford, UK

    Google Scholar 

  11. Connolly I.C., Burns B.J., Jog M.S. (2000): A dynamical-systems model for Parkinson’s disease. Biological Cybernetics 83, 47–59

    Article  MATH  Google Scholar 

  12. Freund, Y., and Schapire, R. E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the Second European Conference on Computational Learning Theory, Barcelona, Spain (1995).

  13. Galarreta M., Hestrin S. (1999): A network of fast-spiking cells in the neocortex connected by electrical synapses. Nature 402, 72–75

    Article  ADS  Google Scholar 

  14. Gerfen, C. R., and Wilson, C. J.: The basal ganglia. In: L. W. Swanson, A. Bjorklund and T. Hokfelt Handbook of Chemical Neuroanatomy Volume 12: Integrated Systems of the CNS, Part IIII, 371–468, Elsevier, London, (1996).

  15. Gerstner W., Kistler W.M. (2002).: Spiking Neuron Models Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  16. Golding N.L., Kath W.L., Spruston N. (2001): Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites. Journal of Neurophysiology 86, 2998–3010

    Google Scholar 

  17. Gray C.M., Maldonado P.E., Wilson M., McNaughton B. (1995): Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. Journal of Neuroscience Methods 63, 43–54

    Article  Google Scholar 

  18. Grossberg S. (1987): Competitive learning: from interactive activation to adaptive resonance. Cognitive Science 11, 23–63

    Article  Google Scholar 

  19. Häusser M., Spruston N., Stuart G.J. (2000): Diversity and dynamics of dendritic signaling. Science 290, 739–744

    Article  ADS  Google Scholar 

  20. Hebb D. (1949). The Organisation of Behaviour. Wiley, New York

    Google Scholar 

  21. Holt G.R., Koch C. (1999): Interactions via the extracellular potential near cell bodies. Journal of Computational Neuroscience 6, 169–184

    Article  MATH  Google Scholar 

  22. Hormuzdi S.G., Pais I., LeBeau F.E.N., Towers S.K., Rozov A., Buhl E.H., Whittington M.A., Monyer H. (2001): Impaired electrical signaling disrupts gamma frequency oscillations in connexin 36-deficient mice. Neuron 31, 487–495

    Article  Google Scholar 

  23. Jog M.S., Connolly C.I. Kubota Y., Iyengar D.R., Garrido L., Harlan R., Graybiel A.M. (2002): Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques. Journal of Neuroscience Methods 117, 141–152

    Article  Google Scholar 

  24. Jog M.S., Kubota Y., Connolly C.I., Hillegaart V., Graybiel A.M. (1999): Building neural representations of habits. Science 286, 1745–1749

    Article  Google Scholar 

  25. Kasuga A., Enoki R., Hashimoto Y., Akiyama H., Kawamura Y., Inoue M., Kudo Y., Miyakawa H. (2003): Optical detection of dendritic spike initiation in hippocampal CA1 pyramidal neurons. Neuroscience 118, 899–907

    Article  Google Scholar 

  26. Kerr J.N.D., Plenz D. (2002): Dendritic calcium encodes striatal neuron output during up-States. Journal of Neuroscience 22, 1499 – 1512

    Google Scholar 

  27. Kimura M., Matsumoto N., Okahashi K., Ueda Y., Satoh T. (2003): Goal-directed, serial and synchronous activation of neurons in the primate striatum. Neuroreport 14(6): 799–802

    Article  Google Scholar 

  28. Kita H., Kosaka T., Heizmann C.W. (1990): Parvalbumin-immunoreactive neurons in the rat neostriatum: a light and electron microscopic study. Brain Research 536, 1–15

    Article  Google Scholar 

  29. Kohonen T. (2001). Self-Organizing Maps. Springer Verlag, Berlin

    MATH  Google Scholar 

  30. Koos T., Tepper J.M. (1999): Inhibitory control of neostriatal projection neurons by GABAergic interneurons. Nature of Neuroscience 2, 467–472

    Article  Google Scholar 

  31. Landisman C.E., Long M.A., Beierlein M., Deans M.R., Paul D.L., Connors B.W. (2002): Electrical synapses in the thalamic reticular nucleus. Journal of Neuroscience 22, 1002–1009

    Google Scholar 

  32. Maass W. (1997): Networks of spiking neurons: the third generation of neural network models. Neural Networks 10, 1659–1671

    Article  Google Scholar 

  33. von der Malsburg C. (1973): Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85–100

    Article  Google Scholar 

  34. Nolan M.F., Malleret G., Dudman J.T., Buhl D.L., Santoro B., Gibbs E., Buzsaki G., Siegelbaum S.A., Kandel E.R., Morozov A.A. (2004) Behavioral role for dendritic integration: HCN1 channels constrain spatial memory and plasticity at inputs to distal dendrites of CA1 pyramidal neurons. Cell 119, 719–732

    Google Scholar 

  35. Oesch N., Euler T., Taylor W.R. (2005) Direction-selective dendritic action potentials in rabbit retina. Neuron 47(5): 739–750

    Article  Google Scholar 

  36. Quirk M.C., Wilson M.A. (1999), Interaction between spike waveform classification and temporal sequence detection. Journal of Neuroscience Methods 94(1): 41–52

    Article  Google Scholar 

  37. Quirk M.C., Blum K.I., Wilson M.A. (2001), Experience-eependent changes in extracellular spike amplitude may reflect regulation of dendritic action potential back-propagation in rat hippocampal pyramidal cells. The Journal of Neuroscience 21(1): 240–248

    Google Scholar 

  38. Rusu, S., Muljono, H., Cherkauer, B.: Itanium 2 processor 6M: higher frequency and larger L3 cache, IEEE Micro, 2004, ieeexplore.ieee.org.

  39. Schapire R.E. (1990), The strength of weak learnability. Machine Learning 5: 197–227

    Google Scholar 

  40. Schapire, R E., Singer, Y., and Singhal, A.: Boosting and Rocchio Applied to text filtering, SIGIR ’98, Proceedings of the Twenty-first Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, (1998), pp. 215–223.

  41. Shannon, C. E., and Weaver, W.: The mathematical theory of communication. University of Illinois Press, IL (ISBN 0252725484)(1963).

  42. Schmitzer-Torbert N., Jackson J., Henze D., Harris K., Redish A.D. (2005), measures of unit quality for use in extracellular recordings. Neuroscience 131(1): 1–11

    Article  Google Scholar 

  43. Stewart G.W. (1993) The early history of the SVD. SIAM Review 35, 558–561

    Google Scholar 

  44. Valiant L.G. (1984), A theory of the learnable. Communications of the ACM 27: 1134–1142

    Article  MATH  Google Scholar 

  45. Verzi, S. J., Heileman, G. L., Georgiopoulos, M., and Healy, M. J.: Boosted ARTMAP in neural networks proceedings, IEEE world congress on computational intelligence. The 1998 IEEE International Joint Conference Anchorage, Ak, USA, 1 (1998), 4–9.

  46. Wilson M.A., McNaughton B.L. (1993) Dynamics of the hippocampal ensemble code for space. Science 261: 1055–1058

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dorian Aur.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aur, D., Jog, M.S. Neuronal spatial learning. Neural Process Lett 25, 31–47 (2007). https://doi.org/10.1007/s11063-006-9029-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-006-9029-2

Keywords

Navigation