How Can We Detect Ensemble Coding by Cell Assembly?

  • Yoshio Sakurai


The present chapter discusses why cell-assembly coding, i.e., ensemble coding by functionally connected neurons, is an appropriate view of the brain’s neuronal code and how it operates in the working brain. The cell-assembly coding has two major properties, i.e., partial overlapping of neurons among assemblies and connection dynamics within and among the assemblies. The former is the ability of one neuron to participate in different types of information processing. The latter is the capability for functional synaptic connections, detected by synchrony of firing of the neurons, to change among different types of information processing. Examples of experiments which detected these two major properties are then given. Several relevant points concerning the detection of cell assemblies and dual-coding by cell assemblies and single neurons are also enumerated. Finally, technical and theoretical improvements necessary for future researches of cell-assembly coding are discussed. They include an unique technique of spike-sorting with independent component analysis and theories of sparse coding by distributed overlapped assemblies and coincidence detection as a role of individual neurons to bind distributed neurons into cell assemblies.


Independent Component Analysis Cell Assembly Single Neuron Independent Component Analysis Individual 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abeles M (1982) Quantification, smoothing, and confidence limits for single-units’ histograms. J Neurosci Methods 5:317–325PubMedCrossRefGoogle Scholar
  2. Abeles M (1988) Neural codes for higher brain functions. In: Markowitsch HJ (ed) Information processing by the brain. Huber, StuttgartGoogle Scholar
  3. Abeles M (1992) Local cortical circuits. Springer, New York.Google Scholar
  4. Abeles M, Bergman H, Margalit E, Vaadia E (1993) Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J Neurophysiol 70:1629–1638PubMedGoogle Scholar
  5. Aertsen A, Braitenberg V (eds) (1996) Brain theory: biological basis and computational principles, Elsevier, AmsterdamGoogle Scholar
  6. Ahissar E, Vaadia E, Ahissar M, Bergman H, Arieli A, Abeles M (1992a) Dependence of cortical plasticity on correlated activity of single neurons and on behavioral context. Science 257:1412–1414PubMedCrossRefGoogle Scholar
  7. Ahissar M, Ahissar E, Bergman H, Vaadia E (1992b) Encoding of sound-source location and movement: activity of single neurons and interactions between adjacent neurons in the monkey auditory cortex. J Neurophysiol 67:203–215PubMedGoogle Scholar
  8. Amit DJ, Brunel N, Tsodyks MV (1994) Correlations of cortical Hebbian reverberations: theory versus experiments. J Neurosci 14:6435–6445PubMedGoogle Scholar
  9. Arieli A, Shoham D, Hildesheim R, Grinvald A (1995) Coherent spatiotemporal patterns of ongoing activity revealed by real-time optical imaging coupled with single-unit recording in the cat visual cortex. J Neurophysiol 73:2072–2093PubMedGoogle Scholar
  10. Barlow HB (1972) Single units and sensation: a doctrine for perceptual psychology? Perception 1:371–394PubMedCrossRefGoogle Scholar
  11. Braitenberg V (1978) Cell assemblies in the cerebral cortex. In: Heim R, Palm G (eds) Theoretical approaches to complex system. Springer, New York, pp 171–188Google Scholar
  12. Calvin WH (1996) The cerebral code: thinking a thought in the mosaics of the mind. MIT Press, CambridgeGoogle Scholar
  13. Cook JE (1991) Correlated activity in the CNS: a role on every timescale? Trends Neurosci 14:397–401PubMedCrossRefGoogle Scholar
  14. de Oliveira SC, Thiele A, Hoffmann K (1997) Synchronization of neuronal activity during stimulus expectation in a direction discrimination task. J Neurosci 17:9248–9260PubMedGoogle Scholar
  15. Douglas RJ, Martin AC (1991) Opening the gray box. Trends Neurosci 14:286–293PubMedCrossRefGoogle Scholar
  16. Eichenbaum H (1993) Thinking about brain cell assemblies. Science 261:993–994PubMedCrossRefGoogle Scholar
  17. Engel AK, König P, Kreiter AK, Schillen TB, Singer W (1992) Temporal coding in the visual cortex: new vistas on integration in the nervous system. Trends Neurosci 6:218–226CrossRefGoogle Scholar
  18. Espinosa IE, Gerstein GL (1988) Cortical auditory neuron interactions during presentation of 3-tone sequence: effective connectivity. Brain Res 450:39–50PubMedCrossRefGoogle Scholar
  19. Fee M, Mitra P, Kleinfeld D (1996) Variability of extracellular spike waveforms of cortical neurons. J Neurophysiol 76:3823–3833PubMedGoogle Scholar
  20. Friesen WO, Friesen JA (1994) NeuroDynamix: computer models for neurophysiology. Oxford University Press, New YorkGoogle Scholar
  21. Fujii H, Ito H, Aihara K, Tsukada M (1996) Dynamical cell assembly hypothesis: theoretical possibility of spatio-temporal coding in the cortex. Neural Netw 9:1303–1350PubMedCrossRefGoogle Scholar
  22. Fujita I, Tanaka K, Ito M, Cheng K (1992) Columns for visual features of objects in monkey inferotemporal cortex. Nature (Lond) 360:343–346PubMedCrossRefGoogle Scholar
  23. Georgopoulos AP (1995) Current issues in directional motor control. Trends Neurosci 18:506–510PubMedCrossRefGoogle Scholar
  24. Gerstein GL, Bedenbaugh P, Aertsen AMHJ (1989) Neural assemblies. IEEE Trans Biomed Eng 36:4–14PubMedCrossRefGoogle Scholar
  25. Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New YorkGoogle Scholar
  26. Hebb DO (1972) Textbook of psychology, 3rd edn. Saunders, Toronto.Google Scholar
  27. John ER (1972) Switchboard versus statistical theories of learning and memory. Science 177:850–864PubMedCrossRefGoogle Scholar
  28. Kanerva P (1988) Sparse distributed memory. MIT Press, CambridgeGoogle Scholar
  29. König P, Engel AK, Singer W (1996) Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci 19:130–137PubMedCrossRefGoogle Scholar
  30. Krüger J, Becker JD (1991) Recognizing the visual stimulus from neuronal discharges. Trends Neurosci 14:281–286CrossRefGoogle Scholar
  31. Legéndy CR, Salcman M (1985) Bursts and recurrences of bursts in the spike train of spontaneously active striate cortex neurons. J Neurophysiol 53:926–939PubMedGoogle Scholar
  32. Lewicki M (1994) Bayesian modelling and classification of neural signals. Neural Comput 6:1005–1030CrossRefGoogle Scholar
  33. Melzack R (1989) Phantom limbs, the self and the brain: The D.O. Hebb memorial lecture. Can Psychol 30:1–16CrossRefGoogle Scholar
  34. Meunier C, Yanai H, Amari S (1991) Sparsely coded associative memories: capacity and dynamical properties. Network 2:469–487CrossRefGoogle Scholar
  35. Miyashita Y, Chang HS (1988) Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature (Lond) 331:68–70PubMedCrossRefGoogle Scholar
  36. Montgomery EB Jr, Clare MH, Sahrmann S, Buchholz SR, Hibbard LS, Landau WM (1992) Neuronal multipotentiality: evidence for network representation of physiological function. Brain Res 580:49–61PubMedCrossRefGoogle Scholar
  37. Paisley AC, Summerlee AJ (1984) Relationship between behavioral states and activity of the cerebral cortex. Prog Neurobiol 22:155–184PubMedCrossRefGoogle Scholar
  38. Palm G (1990) Cell assemblies as a guideline for brain research. Concepts Neurosci 1:133–147Google Scholar
  39. Palm G (1990) Cell assemblies, coherence, and corticohippocampal interplay. Hippocampus 3:219–226Google Scholar
  40. Perkel DH, Gerstein GL, Moore GP (1967) Neuronal spike trains and stochastic point processes. (2) Simultaneous spike trains. Biophys J 7:419–440PubMedCrossRefGoogle Scholar
  41. Pribram KH (1991) Brain and perception: homology and structure in figural processing. Erlbaum, Hillsdale, NJGoogle Scholar
  42. Ramachandran VS, Rogers-Ramachandran D, Stewart M (1992) Perceptual correlates of massive cortical reorganization. Science 258:1159–1160PubMedCrossRefGoogle Scholar
  43. Reyes A, Lujan R, Rozov A, Burnashev N, Somogyi P, Sakmann B (1998) Target-cell-specific facilitation and depression in neocortical circuits. Nature Neurosci 1:279–285PubMedCrossRefGoogle Scholar
  44. Riehle A, Grün S, Diesmann M, Aertsen A (1997) Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278:1950–1953PubMedCrossRefGoogle Scholar
  45. Rieke F, Warland D, van Steveninck RDR, Bialek W (1997) Spikes: Exploring the neural code. MIT Press, CambridgeGoogle Scholar
  46. Sakurai Y (1987) Rat’s auditory working memory tested by continuous non-matching-to-sample performance. Psychobiology 15:277–281Google Scholar
  47. Sakurai Y (1998a) The search for cell assembly in the working brain. Behav Brain Res 91:1–13PubMedCrossRefGoogle Scholar
  48. Sakurai Y (1998b) Cell-assembly coding in several memory processes. Neurobiol Learn Memory 70:212–225CrossRefGoogle Scholar
  49. Sakurai Y (1990a) Hippocampal cells have behavioral correlates during the performance of an auditory working memory task in the rat. Behav Neurosci 104:253–263PubMedCrossRefGoogle Scholar
  50. Sakurai Y (1990b) Cells in the rat auditory system have sensory-delay correlates during the performance of an auditory working memory task. Behav Neurosci 104:856–868PubMedCrossRefGoogle Scholar
  51. Sakurai Y (1992) Auditory working and reference memory can be tested in a single situation of stimuli for the rat. Behav Brain Res 50:193–195PubMedCrossRefGoogle Scholar
  52. Sakurai Y (1993) Dependence of functional synaptic connections of hippocampal and neocortical neurons on types of memory. Neurosci Lett 158:181–184PubMedCrossRefGoogle Scholar
  53. Sakurai Y (1994) Involvement of auditory cortical and hippocampal neurons in auditory working memory and reference memory in the rat. J Neurosci 14:2606–2623PubMedGoogle Scholar
  54. Sakurai Y (1996a) Hippocampal and neocortical cell assemblies encode memory processes for different types of stimuli in the rat. J Neurosci 16:2809–2819PubMedGoogle Scholar
  55. Sakurai Y (1996b) Population coding by cell assemblies—what it really is in the brain. Neurosci Res 26:1–16PubMedGoogle Scholar
  56. Sakurai Y (1999a) How do cell assemblies encode information in the brain? Neurosci Biobehav Rev 23:789–796CrossRefGoogle Scholar
  57. Sakurai Y (1999b) Elemental, configural, and sequential memory processes in the rat can be tested in a single situation in one day. Psychobiology 27:486–490Google Scholar
  58. Sakurai Y (2001) Working memory for temporal and nontemporal events in monkeys. Learn Mem 8:309–316PubMedCrossRefGoogle Scholar
  59. Sakurai Y (2002) Coding of temporal information by hippocampal individual cells and cell assemblies in the rat. Neuroscience 115:1153–1163PubMedCrossRefGoogle Scholar
  60. Sakurai Y, Takahashi S, Inoue M (2004) Stimulus duration in working memory is represented by neuronal activity in the monkey prefrontal cortex. Eur J Neurosci 20:1069–1080PubMedCrossRefGoogle Scholar
  61. Sakurai Y, Takahashi S (2006) Dynamic synchrony of firing in the monkey prefrontal cortex during working memory tasks. J Neurosci 26:10141–10153PubMedCrossRefGoogle Scholar
  62. Schoenbaum G, Eichenbaum H (1995) Information coding in the rodent prefrontal cortex. II. Ensemble activity in orbitofrontal cortex. J Neurophysiol 74:751–762PubMedGoogle Scholar
  63. Shaw GL, Harth E, Scheibel AB (1982) Cooperativity in brain function: assemblies of approximately 30 neurons. Exp Neurol 77:324–358PubMedCrossRefGoogle Scholar
  64. Singer W (1990) The formation of cooperative cell assemblies in the visual cortex. J Exp Biol 153:177–197PubMedGoogle Scholar
  65. Snowden RJ, Treue S, Andersen RA (1992) The response of neurons in area V1 and MT of alert rhesus monkey to moving random dot patterns. Exp Brain Res 88:389–400PubMedCrossRefGoogle Scholar
  66. Softky WR, Koch C (1993) The high irregular firing of cortical cells is inconsistent with temporal integration of random EPEPs. J Neurosci 13:334–350PubMedGoogle Scholar
  67. Steriade M, Contreras D, Curro Dossi R, Nunez A (1993) The slow (<1 Hz) oscillation in reticular thalamic and thalamocortical neurons: scenario of sleep rhythm generation in interacting thalamic and neocortical networks. J Neurosci 13:3284–3299PubMedGoogle Scholar
  68. Stuart G, Schiller J, Sakmann B (1997) Action potential initiation and propagation in rat neocortical pyramidal neurons. J Physiol 505(part 3):617–632PubMedCrossRefGoogle Scholar
  69. Takahashi S, Sakurai Y (2005) Real-time and automatic sorting of multi-neuronal activity for sub-millisecond interactions in vivo. Neuroscience 134:301–315PubMedCrossRefGoogle Scholar
  70. Takahashi S, Anzai Y, Sakurai Y (2003a) Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes. J Neurophysiol 89:2245–2258PubMedCrossRefGoogle Scholar
  71. Takahashi S, Anzai Y, Sakurai Y (2003b) A new approach to spike sorting for multi-neuronal activities recorded with a tetrode: how ICA can be practical. Neurosci Res 46:265–272PubMedCrossRefGoogle Scholar
  72. Tanaka K (1996) Inferotemporal cortex and object vision. Annu Rev Neurosci 19:109–139PubMedCrossRefGoogle Scholar
  73. Toyama K, Kimura M, Tanaka K (1981) Cross-correlational analysis of interneuronal connectivity in cat visual cortex. J Neurophysiol 46:191–201PubMedGoogle Scholar
  74. Vaadia E, Bergman H, Abeles M (1989) Neuronal activities related to higher brain functions: theoretical and experimental implications. IEEE Trans Biomed Eng 36:25–35PubMedCrossRefGoogle Scholar
  75. Vaadia E, Haalman I, Abeles M, Bergman H, Prut Y, Slovin H, Aertsen A (1995) Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature (Lond) 373:515–518PubMedCrossRefGoogle Scholar
  76. von der Malsburg C (1986) Am I thinking assemblies? In: Palm G, Aertsen A (eds) Brain theory. Springer, Berlin, pp 161–176Google Scholar
  77. von der Malsburg C (1988) Pattern recognition by labeled graph matching. Neural Netw 1:141–148Google Scholar
  78. Wang G, Tanaka K, Tanifuji M (1996) Optical imaging of functional organization in the monkey inferotemporal cortex. Science 272:1665–1668PubMedCrossRefGoogle Scholar
  79. Watanabe M, Aihara K, Kondo S (1998) A dynamic neural network with temporal coding and functional connectivity. Biol Cybern 78:87–93PubMedCrossRefGoogle Scholar
  80. Wickelgren WA (1992) Webs, cell assemblies, and chunking in neural nets. Concepts Neurosci 1:1–53Google Scholar
  81. Young MP, Yamane S (1992) Sparse population coding of faces in the inferotemporal cortex. Science 256:1327–1331PubMedCrossRefGoogle Scholar
  82. Yufik YM (1998) Virtual associative networks: a framework for cognitive modeling. In: Pribram KH (ed) Brain and values. Erlbaum, Mahwah, pp 109–178Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Yoshio Sakurai
    • 1
    • 2
  1. 1.Department of Psychology, Graduate School of LettersKyoto UniversityKyotoJapan
  2. 2.Core Research for Evolution Science and Technology (CREST)Japan Science and Technology AgencyKawaguchiJapan

Personalised recommendations