Skip to main content
SpringerLink
Log in

How Can We Detect Ensemble Coding by Cell Assembly?

  • Chapter
Representation and Brain

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Abeles M (1982) Quantification, smoothing, and confidence limits for single-units’ histograms. J Neurosci Methods 5:317–325

    Article  PubMed  CAS  Google Scholar 

  • Abeles M (1988) Neural codes for higher brain functions. In: Markowitsch HJ (ed) Information processing by the brain. Huber, Stuttgart

    Google Scholar 

  • Abeles M (1992) Local cortical circuits. Springer, New York.

    Google Scholar 

  • Abeles M, Bergman H, Margalit E, Vaadia E (1993) Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J Neurophysiol 70:1629–1638

    PubMed  CAS  Google Scholar 

  • Aertsen A, Braitenberg V (eds) (1996) Brain theory: biological basis and computational principles, Elsevier, Amsterdam

    Google Scholar 

  • 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 257:1412–1414

    Article  PubMed  CAS  Google Scholar 

  • 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 67:203–215

    PubMed  CAS  Google Scholar 

  • Amit DJ, Brunel N, Tsodyks MV (1994) Correlations of cortical Hebbian reverberations: theory versus experiments. J Neurosci 14:6435–6445

    PubMed  CAS  Google Scholar 

  • 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 73:2072–2093

    PubMed  CAS  Google Scholar 

  • Barlow HB (1972) Single units and sensation: a doctrine for perceptual psychology? Perception 1:371–394

    Article  PubMed  CAS  Google Scholar 

  • Braitenberg V (1978) Cell assemblies in the cerebral cortex. In: Heim R, Palm G (eds) Theoretical approaches to complex system. Springer, New York, pp 171–188

    Google Scholar 

  • Calvin WH (1996) The cerebral code: thinking a thought in the mosaics of the mind. MIT Press, Cambridge

    Google Scholar 

  • Cook JE (1991) Correlated activity in the CNS: a role on every timescale? Trends Neurosci 14:397–401

    Article  PubMed  CAS  Google Scholar 

  • de Oliveira SC, Thiele A, Hoffmann K (1997) Synchronization of neuronal activity during stimulus expectation in a direction discrimination task. J Neurosci 17:9248–9260

    PubMed  Google Scholar 

  • Douglas RJ, Martin AC (1991) Opening the gray box. Trends Neurosci 14:286–293

    Article  PubMed  CAS  Google Scholar 

  • Eichenbaum H (1993) Thinking about brain cell assemblies. Science 261:993–994

    Article  PubMed  CAS  Google Scholar 

  • 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 6:218–226

    Article  Google Scholar 

  • Espinosa IE, Gerstein GL (1988) Cortical auditory neuron interactions during presentation of 3-tone sequence: effective connectivity. Brain Res 450:39–50

    Article  PubMed  CAS  Google Scholar 

  • Fee M, Mitra P, Kleinfeld D (1996) Variability of extracellular spike waveforms of cortical neurons. J Neurophysiol 76:3823–3833

    PubMed  CAS  Google Scholar 

  • Friesen WO, Friesen JA (1994) NeuroDynamix: computer models for neurophysiology. Oxford University Press, New York

    Google Scholar 

  • Fujii H, Ito H, Aihara K, Tsukada M (1996) Dynamical cell assembly hypothesis: theoretical possibility of spatio-temporal coding in the cortex. Neural Netw 9:1303–1350

    Article  PubMed  Google Scholar 

  • Fujita I, Tanaka K, Ito M, Cheng K (1992) Columns for visual features of objects in monkey inferotemporal cortex. Nature (Lond) 360:343–346

    Article  PubMed  CAS  Google Scholar 

  • Georgopoulos AP (1995) Current issues in directional motor control. Trends Neurosci 18:506–510

    Article  PubMed  CAS  Google Scholar 

  • Gerstein GL, Bedenbaugh P, Aertsen AMHJ (1989) Neural assemblies. IEEE Trans Biomed Eng 36:4–14

    Article  PubMed  CAS  Google Scholar 

  • Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York

    Google Scholar 

  • Hebb DO (1972) Textbook of psychology, 3rd edn. Saunders, Toronto.

    Google Scholar 

  • John ER (1972) Switchboard versus statistical theories of learning and memory. Science 177:850–864

    Article  PubMed  CAS  Google Scholar 

  • Kanerva P (1988) Sparse distributed memory. MIT Press, Cambridge

    Google Scholar 

  • König P, Engel AK, Singer W (1996) Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci 19:130–137

    Article  PubMed  Google Scholar 

  • KrĂĽger J, Becker JD (1991) Recognizing the visual stimulus from neuronal discharges. Trends Neurosci 14:281–286

    Article  Google Scholar 

  • LegĂ©ndy CR, Salcman M (1985) Bursts and recurrences of bursts in the spike train of spontaneously active striate cortex neurons. J Neurophysiol 53:926–939

    PubMed  Google Scholar 

  • Lewicki M (1994) Bayesian modelling and classification of neural signals. Neural Comput 6:1005–1030

    Article  Google Scholar 

  • Melzack R (1989) Phantom limbs, the self and the brain: The D.O. Hebb memorial lecture. Can Psychol 30:1–16

    Article  Google Scholar 

  • Meunier C, Yanai H, Amari S (1991) Sparsely coded associative memories: capacity and dynamical properties. Network 2:469–487

    Article  Google Scholar 

  • Miyashita Y, Chang HS (1988) Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature (Lond) 331:68–70

    Article  PubMed  CAS  Google Scholar 

  • 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 580:49–61

    Article  PubMed  Google Scholar 

  • Paisley AC, Summerlee AJ (1984) Relationship between behavioral states and activity of the cerebral cortex. Prog Neurobiol 22:155–184

    Article  PubMed  CAS  Google Scholar 

  • Palm G (1990) Cell assemblies as a guideline for brain research. Concepts Neurosci 1:133–147

    Google Scholar 

  • Palm G (1990) Cell assemblies, coherence, and corticohippocampal interplay. Hippocampus 3:219–226

    Google Scholar 

  • Perkel DH, Gerstein GL, Moore GP (1967) Neuronal spike trains and stochastic point processes. (2) Simultaneous spike trains. Biophys J 7:419–440

    Article  PubMed  CAS  Google Scholar 

  • Pribram KH (1991) Brain and perception: homology and structure in figural processing. Erlbaum, Hillsdale, NJ

    Google Scholar 

  • Ramachandran VS, Rogers-Ramachandran D, Stewart M (1992) Perceptual correlates of massive cortical reorganization. Science 258:1159–1160

    Article  PubMed  CAS  Google Scholar 

  • Reyes A, Lujan R, Rozov A, Burnashev N, Somogyi P, Sakmann B (1998) Target-cell-specific facilitation and depression in neocortical circuits. Nature Neurosci 1:279–285

    Article  PubMed  CAS  Google Scholar 

  • Riehle A, GrĂĽn S, Diesmann M, Aertsen A (1997) Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278:1950–1953

    Article  PubMed  CAS  Google Scholar 

  • Rieke F, Warland D, van Steveninck RDR, Bialek W (1997) Spikes: Exploring the neural code. MIT Press, Cambridge

    Google Scholar 

  • Sakurai Y (1987) Rat’s auditory working memory tested by continuous non-matching-to-sample performance. Psychobiology 15:277–281

    Google Scholar 

  • Sakurai Y (1998a) The search for cell assembly in the working brain. Behav Brain Res 91:1–13

    Article  PubMed  CAS  Google Scholar 

  • Sakurai Y (1998b) Cell-assembly coding in several memory processes. Neurobiol Learn Memory 70:212–225

    Article  CAS  Google Scholar 

  • Sakurai Y (1990a) Hippocampal cells have behavioral correlates during the performance of an auditory working memory task in the rat. Behav Neurosci 104:253–263

    Article  PubMed  CAS  Google Scholar 

  • Sakurai Y (1990b) Cells in the rat auditory system have sensory-delay correlates during the performance of an auditory working memory task. Behav Neurosci 104:856–868

    Article  PubMed  CAS  Google Scholar 

  • Sakurai Y (1992) Auditory working and reference memory can be tested in a single situation of stimuli for the rat. Behav Brain Res 50:193–195

    Article  PubMed  CAS  Google Scholar 

  • Sakurai Y (1993) Dependence of functional synaptic connections of hippocampal and neocortical neurons on types of memory. Neurosci Lett 158:181–184

    Article  PubMed  CAS  Google Scholar 

  • Sakurai Y (1994) Involvement of auditory cortical and hippocampal neurons in auditory working memory and reference memory in the rat. J Neurosci 14:2606–2623

    PubMed  CAS  Google Scholar 

  • Sakurai Y (1996a) Hippocampal and neocortical cell assemblies encode memory processes for different types of stimuli in the rat. J Neurosci 16:2809–2819

    PubMed  CAS  Google Scholar 

  • Sakurai Y (1996b) Population coding by cell assemblies—what it really is in the brain. Neurosci Res 26:1–16

    PubMed  CAS  Google Scholar 

  • Sakurai Y (1999a) How do cell assemblies encode information in the brain? Neurosci Biobehav Rev 23:789–796

    Article  Google Scholar 

  • Sakurai Y (1999b) Elemental, configural, and sequential memory processes in the rat can be tested in a single situation in one day. Psychobiology 27:486–490

    Google Scholar 

  • Sakurai Y (2001) Working memory for temporal and nontemporal events in monkeys. Learn Mem 8:309–316

    Article  PubMed  CAS  Google Scholar 

  • Sakurai Y (2002) Coding of temporal information by hippocampal individual cells and cell assemblies in the rat. Neuroscience 115:1153–1163

    Article  PubMed  CAS  Google Scholar 

  • 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 20:1069–1080

    Article  PubMed  Google Scholar 

  • Sakurai Y, Takahashi S (2006) Dynamic synchrony of firing in the monkey prefrontal cortex during working memory tasks. J Neurosci 26:10141–10153

    Article  PubMed  CAS  Google Scholar 

  • Schoenbaum G, Eichenbaum H (1995) Information coding in the rodent prefrontal cortex. II. Ensemble activity in orbitofrontal cortex. J Neurophysiol 74:751–762

    PubMed  CAS  Google Scholar 

  • Shaw GL, Harth E, Scheibel AB (1982) Cooperativity in brain function: assemblies of approximately 30 neurons. Exp Neurol 77:324–358

    Article  PubMed  CAS  Google Scholar 

  • Singer W (1990) The formation of cooperative cell assemblies in the visual cortex. J Exp Biol 153:177–197

    PubMed  CAS  Google Scholar 

  • 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 88:389–400

    Article  PubMed  CAS  Google Scholar 

  • Softky WR, Koch C (1993) The high irregular firing of cortical cells is inconsistent with temporal integration of random EPEPs. J Neurosci 13:334–350

    PubMed  CAS  Google Scholar 

  • 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 13:3284–3299

    PubMed  CAS  Google Scholar 

  • Stuart G, Schiller J, Sakmann B (1997) Action potential initiation and propagation in rat neocortical pyramidal neurons. J Physiol 505(part 3):617–632

    Article  PubMed  CAS  Google Scholar 

  • Takahashi S, Sakurai Y (2005) Real-time and automatic sorting of multi-neuronal activity for sub-millisecond interactions in vivo. Neuroscience 134:301–315

    Article  PubMed  CAS  Google Scholar 

  • Takahashi S, Anzai Y, Sakurai Y (2003a) Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes. J Neurophysiol 89:2245–2258

    Article  PubMed  Google Scholar 

  • 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 46:265–272

    Article  PubMed  Google Scholar 

  • Tanaka K (1996) Inferotemporal cortex and object vision. Annu Rev Neurosci 19:109–139

    Article  PubMed  CAS  Google Scholar 

  • Toyama K, Kimura M, Tanaka K (1981) Cross-correlational analysis of interneuronal connectivity in cat visual cortex. J Neurophysiol 46:191–201

    PubMed  CAS  Google Scholar 

  • Vaadia E, Bergman H, Abeles M (1989) Neuronal activities related to higher brain functions: theoretical and experimental implications. IEEE Trans Biomed Eng 36:25–35

    Article  PubMed  CAS  Google Scholar 

  • 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) 373:515–518

    Article  PubMed  CAS  Google Scholar 

  • von der Malsburg C (1986) Am I thinking assemblies? In: Palm G, Aertsen A (eds) Brain theory. Springer, Berlin, pp 161–176

    Google Scholar 

  • von der Malsburg C (1988) Pattern recognition by labeled graph matching. Neural Netw 1:141–148

    Google Scholar 

  • Wang G, Tanaka K, Tanifuji M (1996) Optical imaging of functional organization in the monkey inferotemporal cortex. Science 272:1665–1668

    Article  PubMed  CAS  Google Scholar 

  • Watanabe M, Aihara K, Kondo S (1998) A dynamic neural network with temporal coding and functional connectivity. Biol Cybern 78:87–93

    Article  PubMed  CAS  Google Scholar 

  • Wickelgren WA (1992) Webs, cell assemblies, and chunking in neural nets. Concepts Neurosci 1:1–53

    Google Scholar 

  • Young MP, Yamane S (1992) Sparse population coding of faces in the inferotemporal cortex. Science 256:1327–1331

    Article  PubMed  CAS  Google Scholar 

  • Yufik YM (1998) Virtual associative networks: a framework for cognitive modeling. In: Pribram KH (ed) Brain and values. Erlbaum, Mahwah, pp 109–178

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, 606-8501, Japan

    Yoshio Sakurai

  2. Core Research for Evolution Science and Technology (CREST), Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan

    Yoshio Sakurai

Authors
  1. Yoshio Sakurai
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Kokoro Research Center, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan

    Shintaro Funahashi Ph.D. (Professor) (Professor)

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer

About this chapter

Cite this chapter

Sakurai, Y. (2007). How Can We Detect Ensemble Coding by Cell Assembly?. In: Funahashi, S. (eds) Representation and Brain. Springer, Tokyo. https://doi.org/10.1007/978-4-431-73021-7_10

Download citation

Publish with us

Policies and ethics

Search

Navigation