Advertisement

Atoms of Mind pp 101-155 | Cite as

Carriers and Repositories of Thought

  • W. R. KlemmEmail author
Chapter
  • 783 Downloads

Abstract

Brains contain circuits and connecting pathways within and between circuits. Every circuit contains a group of interconnected neurons that can be recruited as a functional unit for containing the neural signals that constitute any given thought. Thus, the brain thinks with circuitry—within circuits, between circuits, and among circuits. Collectively, the circuits constitute a network, the network of mind.

Keywords

Receptive Field Field Potential Spike Train Rate Code Inhibitory Neuron 
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.

References

  1. Achlioptas, D., D’Souge, R. M., & Spencer, J. (2009). Explosive percolation in random networks. Science, 323, 1453–1455.PubMedCrossRefGoogle Scholar
  2. Adey, W. R. (1988). The cellular microenvironment and signaling through cell membranes. In M. E. O’Connor & R. H. Lovely (Eds.), Electromagnetic fields and neurobehavioral function, Vol. 27: Progress in clinical and biological research (pp. 81–106). New York: Alan R. Liss.Google Scholar
  3. Barco, A., Bailey, C. H., & Kandel, E. R. (2006). Common molecular mechanisms in explicit and implicit memory. Journal of Neurochemistry, 97, 1520–1533.PubMedCrossRefGoogle Scholar
  4. Davie, J. T., Clark, B. A., & Häuser, M. (2008). The origin of the complex spike in cerebellar Purkinje cells. Journal of Neuroscience, 28(30), 7599–7609.PubMedCrossRefGoogle Scholar
  5. Destexhe, A., Conteras, D., & Steriade, M. (1998). Spatio-temporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. Journal of Neuroscience, 19, 4595–4608.Google Scholar
  6. Diaz, J., et al. (2007). Amplitude modulation patterns of local field potentials reveal asynchronous neuronal populations. Journal of Neuroscience, 27(34), 9238–9245.PubMedCrossRefGoogle Scholar
  7. DiLorenzo, P. M., Chen, J. Y., & Victor, J. D. (2009). Quality time: Representation of a multidimensional sensory domain through temporal coding. Journal of Neuroscience, 29(2), 9227–9238.CrossRefGoogle Scholar
  8. Foffani, G., Morales-Botello, M. L., & Aguilar, J. (2009). Spike timing, spike count, and temporal information for the discrimination of tactile stimuli in the rat ventrobasal complex. Journal of Neuroscience, 29(18), 5964–5973.PubMedCrossRefGoogle Scholar
  9. Hagmann, P., et al. (2008). Mapping the structural core of human cerebral cortex. PLOS Biology, 6 (7),e159: 0001–0015.CrossRefGoogle Scholar
  10. Han, J.-H. (2007). Neuronal competition and selection during memory formation. Science, 316, 457–460.PubMedCrossRefGoogle Scholar
  11. Hickok, G., & Poeppel, D. (2007). The cortical organization of speech processing. Nature Reviews. Neuroscience, 8, 393–402.PubMedCrossRefGoogle Scholar
  12. Hiroshi Suzuki, H., et al. (2008). Functional asymmetry in Caenorhabditis elegans taste neurons and its computational role in chemotaxis. Nature, 454, 114. doi:10.1038/nature06927.PubMedCrossRefGoogle Scholar
  13. Hueretz, C. P., Philibert, B., & Edeline, J.-M. (2009). A spike-timing code for discriminating conspecific vocalizations in the thalamocortical system of anesthetized and awake guinea pigs. Journal of Neuroscience, 29(2), 334–350.CrossRefGoogle Scholar
  14. Klemm, W. R. (1969). Animal electroencephalography. New York: Academic.Google Scholar
  15. Klemm, W. R. (1972). Ascending and descending excitatory influences in the brain stem reticulum: A re-examination. Brain Research, 36, 444–452.PubMedCrossRefGoogle Scholar
  16. Klemm, W. R. (1976a). Physiological and behavioral significance of hippocampal rhythmic, slow activity (“theta rhythm”). Progress in Neurobiology, 6, 23–47.PubMedCrossRefGoogle Scholar
  17. Klemm, W. R. (1976b). Hippocampal EEG and information processing: A special role for theta rhythm. Progress in Neurobiology, 7, 197–214.PubMedCrossRefGoogle Scholar
  18. Klemm, W. R. (2004). Habenular and interpeduncularis nuclei: Shared components in multiple-function networks. Medical Science Monitor, 10(11), RA261–RA273.PubMedGoogle Scholar
  19. Klemm, W. R., & Sherry, C. J. (1981a). Entropy measures of signal in the presence of noise: Evidence for “byte” versus “bit” processing in the nervous system. Experientia, 3, 55–58.CrossRefGoogle Scholar
  20. Klemm, W. R., & Sherry, C. J. (1981b). Serial ordering in spike trains: What’s it “trying to tell us?”. International Journal of Neuroscience, 14, 15–33.PubMedCrossRefGoogle Scholar
  21. Klemm, W. R., & Sherry, C. J. (1982). Do neurons process information by relative intervals in spike trains? Neuroscience and Biobehavioral Reviews, 6, 429–437.PubMedCrossRefGoogle Scholar
  22. Klemm, W. R., et al. (1980). Hemispheric lateralization and handedness correlation of human evoked “steady-state” responses to patterned visual stimuli. Physiological Psychology, 8(3), 409–416.Google Scholar
  23. Klemm, W. R., et al. (1982). Differences among humans in steady-state evoked potentials: Evaluation of alpha activity, attentiveness and cognitive awareness of perceptual effectiveness. Neuropsychologia, 20(3), 317–325.PubMedCrossRefGoogle Scholar
  24. Koch, C. (2004). The quest for consciousness. Englewood: Roberts & Company.Google Scholar
  25. Lakatos, P., et al. (2008). Entrainment of neuronal oscillations as a mechanism of attentional selection. Science, 320, 110–113.PubMedCrossRefGoogle Scholar
  26. Le Van Quyen, M., et al. (2008). Cell type-specific firing during ripple oscillations in the hippocampal formation of humans. Journal of Neuroscience, 28(24), 6104–6110.PubMedCrossRefGoogle Scholar
  27. Lestienne, R. (2001). Spike timing, synchronization and information processing on the sensory side of the central nervous system. Progress in Neurobiology, 65, 545–591.PubMedCrossRefGoogle Scholar
  28. Lopez-Fernandez, M. A., et al. (2007). Up regulation of polysialylate4d neural cell adhesion molecule in the dorsal hippocampus after contextual fear conditioning is involved in long-term memory function. Journal of Neuroscience, 27(17), 4552–4561.PubMedCrossRefGoogle Scholar
  29. Luo, H., & Poeppel, D. (2007). Phase patterns of neuronal responses reliably discriminate speech inhuman auditory cortex. Neuron, 54, 1001–1010.PubMedCrossRefGoogle Scholar
  30. Masse, N. Y., & Cook, E. P. (2008). The effect of middle temporal spike phase on sensory encoding and correlates with behavior during a motion-detection task. Journal of Neuroscience, 28(6), 1343–1355.PubMedCrossRefGoogle Scholar
  31. Melzack, R., & Wall, P. D. (1965). Pain mechanisms: A new theory. Science, 150, 971–979.PubMedCrossRefGoogle Scholar
  32. Mikeska, J. A., & Klemm, W. R. (1975). EEG evaluation of humaneness of asphyxia and decapitation euthanasia of the laboratory rat. Laboratory Animal Care, 25, 175–179.Google Scholar
  33. Mongillo, G., Barak, O., & Tsodyks, M. (2008). Synaptic theory of working memory. Science, 319, 1543–1546.PubMedCrossRefGoogle Scholar
  34. Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, 120(4), 701–722.PubMedCrossRefGoogle Scholar
  35. Nakahama, H. (1977a). Dependency as a measure to estimate the order and the values of Markov process. Biological Cybernetics, 25, 209–226.CrossRefGoogle Scholar
  36. Nakahama, H. (1977b). Dependency representing Markov properties of spike trains recorded from central neurons. Tohoku Journal of Experimental Medicine, 122, 99–111.PubMedCrossRefGoogle Scholar
  37. Nakahama, H., et al. (1972a). Markov process of maintained impulse activity in central single neurons. Kybernetik, 11, 61–72.PubMedGoogle Scholar
  38. Nakahama, H., et al. (1972b). Statistical inference of Markov process of neuronal impulse sequences. Kybernetik, 15, 47–64.CrossRefGoogle Scholar
  39. Ray, S., et al. (2008). Neural correlates of high gamma oscillations (60–200 HZ) in macque local field potentials and their potential implications in electrocorticography. Journal of Neuroscience, 28, 11526–11536.PubMedCrossRefGoogle Scholar
  40. Roopun, A. K., et al. (2008). Temporal interactions between cortical rhythms. Frontiers in Neuroscience, 2, 145–154.PubMedCrossRefGoogle Scholar
  41. Rudolpher, S. M., & May, H. U. (1975). On Markov properties of inter-spike times in the cat optic tract. Biological Cybernetics, 19, 197–199.CrossRefGoogle Scholar
  42. Senior, T. J., et al. (2008). Gamma oscillatory firing reveals distinct populations of pyramidal cells in the CA1 region of the hippocampus. Journal of Neuroscience, 28(9), 2274–2286.PubMedCrossRefGoogle Scholar
  43. Sherry, C. J., & Klemm, W. R. (1980). Entropy correlations with drug-induced changes in specified patterns of nerve impulses: Evidence for “byte” processing in the nervous system. Progress in Neuro-Psychopharmacology, 4, 261–267.PubMedCrossRefGoogle Scholar
  44. Sherry, C. J., & Klemm, W. R. (1984). What is the meaningful measure of neuronal spike train activity? Journal of Neuroscience Methods, 10, 205–213.PubMedCrossRefGoogle Scholar
  45. Sherry, C. J., Marczynski, T. J., & Wolf, D. J. (1972). The interdependence series matrix: A method for determining the serial dependence of neuronal interspike intervals. International Journal of Neuroscience, 3, 35–42.PubMedCrossRefGoogle Scholar
  46. Sherry, C. J., Barrow, D. L., & Klemm, W. R. (1982). Serial dependencies and Markov properties of neuronal interspike intervals from rat cerebellum. Brain Research Bulletin, 8, 163–169.PubMedCrossRefGoogle Scholar
  47. Steriade, M., Amzica, F., & Contreras, D. (1996a). Synchronization of fast 30–40 Hz spontaneous cortical rhythms during brain activation. Journal of Neuroscience, 16(1), 392–417.PubMedGoogle Scholar
  48. Steriade, M., Contreras, D., Amzica, F., & Timofeev, I. (1996b). Synchronization of fast (30–40 Hz) spontaneous oscillations in intrathalamic and thalamocortical networks. Journal of Neuroscience, 16(8), 2788–2808.PubMedGoogle Scholar
  49. Steriade, M., Timofeev, I., Dürmüller, N., & Grenier, F. (1998). Dynamic properties of corticothalamic neurons and local cortical interneurons generating fast rhythmic (30–40 Hz) spike bursts. Journal of Neurophysiology, 79, 483–490.PubMedGoogle Scholar
  50. Tero, A., et al. (2010). Rules for biologically inspired adaptive network design. Science, 327, 439–442.PubMedCrossRefGoogle Scholar
  51. Thivierge, J.-P., & Cisek, P. (2008). Nonperiodic synchronization in heterogeneous networks of spiking neurons. Journal of Neuroscience, 28(32), 7968–7978.PubMedCrossRefGoogle Scholar
  52. Werner-Reiss, U., & Groh, J. M. (2008). A rate code for sound azimuth in monkey auditory cortex: Implications for human neuroimaging studies. Journal of Neuroscience, 28(14), 3747–3758.PubMedCrossRefGoogle Scholar
  53. Whitlock, J. R., et al. (2006). Learning induces long-term potentiation in the hippocampus. Science, 313, 1093–1097.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.College of Veterinary Medicine and BiomeCollege StationUSA

Personalised recommendations