Skip to main content

Mechanisms of Cortical Computation

  • Chapter
Neural Engineering

Part of the book series: Bioelectric Engineering ((BEEG))

Abstract

The purpose of this chapter is to explore the computational principles underlying cortical function. We will consider ideas proposed in a large number of recent theoretical models that present a range of interesting, and sometimes conflicting, mechanisms. We will try to tie these theoretical principles to the underlying biology, and will spend most of our time considering the link between the intrinsic properties of neurons and the informationprocessing abilities of cortical circuits. We will consider computations carried out across different cortical areas, associated with processes ranging from sensory detection in vision, audition, and olfaction, to recognition, memory, and categorization.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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

  • Abbott, L. F., 2001, The timing game, Nat. Neurosci. 4:115–116.

    Article  Google Scholar 

  • Aertsen, A., Diesmann, M., and Gewaltig, M. O., 1996, Propagation of synchronous spiking activity in feedforward neural networks, J. Physiol. Paris 90:243–247.

    Article  Google Scholar 

  • Ahlstrom, V., Blake, R., and Ahlstrom, U., 1997, Perception of biological motion, Perception 26:1539–1548.

    Article  Google Scholar 

  • Barlow, H. B., and Levick, W. R., 1965, The mechanism of directionally selective units in rabbit’s retina, J. Physiol. 178:477–504.

    Google Scholar 

  • Beierlein, M., Gibson, J. R., and Connors, B. W., 2000, A network of electrically coupled interneurons drives synchronized inhibition in neocortex, Nat. Neurosci. 3:904–910.

    Article  Google Scholar 

  • Best, P. J., White, A. M., and Minai, A., 2001, Spatial processing in the brain: The activity of hippocampal place cells, Annu. Rev. Neurosci. 24:459–486.

    Article  Google Scholar 

  • Boahen, K. A., 2002, A retinomorphic chip with parallel pathways: Encoding ON, OFF, Increasing, and Decreasing visual signals, J. Analog Integr. Circ. Signal Process. 30:121–135.

    Article  Google Scholar 

  • Bullier, J., 2001, Integrated model of visual processing, Brain Res. Brain Res. Rev. 36:96–107.

    Article  Google Scholar 

  • Buzsaki, G., 2002, Theta oscillations in the hippocampus, Neuron 33:325–340.

    Article  Google Scholar 

  • Churchland, P. S., and Sejnowski, T. J., 1992, The Computational Brain, MIT Press, Cambridge.

    Google Scholar 

  • Das, S., and Finkel, L. H., 2003, Cortical integration of bottom-up, top-down and horizontal information in biological motion recognition, in: Proceedings of 1st International IEEE EMBS Neuroengineering Conference, Capri.

    Google Scholar 

  • Douglas, R. J., and Martin, K. A., 1991, A functional microcircuit for cat visual cortex, J. Physiol. 440:735–769.

    Google Scholar 

  • Edelman, G., 1989, Neural Darwinism, Basic Books, New York.

    Google Scholar 

  • Erisir, A., Lau, D., Rudy, B., and Leonard, C. S., 1999, Function of specific K(+) channels in sustained high-frequency firing of fast-spiking neocortical interneurons, J. Neurophysiol. 82:2476–2489.

    Google Scholar 

  • Gold, J. I., and Shadlen, M. N., 2001, Neural computations that underlie decisions about sensory stimuli, Trends Cogn. Sci. 5:10–16.

    Article  Google Scholar 

  • Grossman, E. D., and Blake, R., 2002, Brain areas active during visual perception of biological motion, Neuron 35:1167–1175.

    Article  Google Scholar 

  • Harris, K. D., Henze, D. A., Hirase, H., Leinekugel, X., Dragoi, G., Czurko, A., and Buzsaki, G., 2002. Spike train dynamics predicts theta-related phase precession in hippocampal pyramidal cells, Nature 417:738–741.

    Article  Google Scholar 

  • Hebb, D. O., 1949, The Organization of Behavior: A Neuropsychological Theory. Wiley, New York.

    Google Scholar 

  • Hopfield, J. J., 1995, Pattern recognition computation using action potential timing for stimulus representation, Nature 376:33–36.

    Article  Google Scholar 

  • Hopfield, J. J., and Brody, C. D., 2000, What is a moment? “Cortical” sensory integration over a brief interval, Proc. Natl. Acad. Sci. USA 97:13919–13924.

    Article  Google Scholar 

  • Hopfield, J. J., and Brody, C. D., 2001, What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration, Proc. Natl. Acad. Sci. USA 98:1282–1287.

    Article  Google Scholar 

  • Ito, M., and Gilbert, C. D., 1999, Attention modulates contextual influences in the primary visual cortex of alert monkeys, Neuron 22:593–604.

    Article  Google Scholar 

  • Kamondi, A., Acsady, L., Wang, X. J., and Buzsaki, G., 1998, Theta oscillations in somata and dendrites of hippocampal pyramidal cells in vivo: Activity-dependent phase-precession of action potentials, Hippocampus 8:244–261.

    Article  Google Scholar 

  • Keysers, C., Xiao, D. K., Foldiak, P., and Perrett, D. I., 2001, The speed of sight, J. Cogn. Neurosci. 13:90–101.

    Article  Google Scholar 

  • Laurent, G., Wehr, M., and Davidowitz, H., 1996, Temporal representations of odors in an olfactory network, J. Neurosci. 16:3837–3847.

    Google Scholar 

  • Laurent, G., Stopfer, M., Friedrich, R. W., Rabinovich, M. I., Volkovskii, A., and Arbarbanel, H. D. I., 2001. Odor encoding as an active dynamical process: Experiments, computation, and theory, Annnu. Rev. Neurosci. 24:263–297.

    Article  Google Scholar 

  • Lisman, J. E., and Otmakhova, N. A., 2001, Storage, recall, and novelty detection of sequences by the hippocampus: Elaborating on the SOCRATIC model to account for normal and aberrant effects of dopamine, Hippocampus 11:551–568.

    Article  Google Scholar 

  • Llinas, R. R., 1988, The intrinsic electrophysiological properties of mammalian neurons: Insights into central nervous system function, Science 242:1654–1664.

    Article  Google Scholar 

  • Maas, W., ed. 1999, Computation with Spiking Neurons, MIT Press, Cambridge.

    Google Scholar 

  • Magee, J. C., 2001, Dendritic mechanisms of phase precession in hippocampal CA1 pyramidal neurons, J. Neurophysiol. 86:528–532.

    Google Scholar 

  • Malik, J., and Perona, P., 1990, Preattentive texture discrimination with early vision mechanisms, J. Opt. Soc. Am. A 7:923–932.

    Google Scholar 

  • Markram, H., Lubke, J., Frotscher, M., and Sakmann, B., 1997, Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs, Science 275:213–215.

    Article  Google Scholar 

  • Miller, D. A., and Zucker, S. W., 1999, Computing with self-excitatory cliques: A model and an application to hyperacuity-scale computation in visual cortex, Neural. Comput. 11:21–66.

    Article  Google Scholar 

  • Petersen, R. S., Panzeri, S., and Diamond, M. E., 2002, The role of individual spikes and spike patterns in population coding of stimulus location in rat somatosensory cortex, Biosystems 67:187–193.

    Article  Google Scholar 

  • Pearl, J., 1988, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Poggio, T., and Reichardt, W., 1973, Considerations on models of movement detection, Kybernetik 13:223–227.

    Article  Google Scholar 

  • Raizada, R. D., and Grossberg, S., 2003, Towards a theory of the laminar architecture of cerebral cortex: Computational clues from the visual system, Cereb. Cortex 13:100–113.

    Article  Google Scholar 

  • Ramachandran, V. S., 1985, The neurobiology of perception, Perception 14:97–103.

    Google Scholar 

  • Rao, R. P., and Ballard, D. H., 1997, Dynamic model of visual recognition predicts neural response properties in the visual cortex, Neural Comput. 9:721–763.

    Article  Google Scholar 

  • Rao, R. P., and Sejnowski, T. J., 2001, Spike-timing-dependent Hebbian plasticity as temporal difference learning, Neural Comput. 13:2221–2237.

    Article  MATH  Google Scholar 

  • Sajda, P., and Finkel, L. H., 1995, Intermediate-level visual representations and the construction of surface perception, J. Cogn. Neurosci. 7:267–291.

    Google Scholar 

  • Saul, A. B., and Humphrey, A. L., 1990, Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus, J. Neurophysiol. 64:206–224.

    Google Scholar 

  • Sutton, R. S., and Barto, A. B., 1998, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA.

    Google Scholar 

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

    Article  Google Scholar 

  • van Santen, J. P., and Sperling, G., 1985, Elaborated Reichardt detectors, J. Opt. Soc. Am. A 2:300–321.

    Google Scholar 

  • VanRullen, R., and Thorpe, S. J., 2002, Surfing a spike wave down the ventral stream, Vision Res. 42:2593–2615.

    Article  Google Scholar 

  • Watson, A. B., and Ahumada, A. J., Jr., 1985. Model of human visual-motion sensing, J. Opt. Soc. Am. A 2:322–341.

    Article  Google Scholar 

  • Weiss, Y., 1997, Interpreting images by propagating Bayesian beliefs, Adv. Neural Inform. Process. Syst. 9:908–915.

    Google Scholar 

  • Whittington, M. A., Traub, R. D., Kopell, N., Ermentrout, B., and Buhl, E. H., 2000, Inhibition-based rhythms: Experimental and mathematical observations on network dynamics, Int. J. Psychophysiol. 38:315–336.

    Article  Google Scholar 

  • Wilson, M. A., and McNaughton, B. L., 1994, Reactivation of hippocampal ensemble memories during sleep, Science 265:676–679.

    Article  Google Scholar 

  • Zanker, J. M., Srinivasan, M. V., and Egelhaaf, M., 1999, Speed tuning in elementary motion detectors of the correlation type, Biol. Cybern. 80:109–116.

    Article  MATH  Google Scholar 

  • Zeki, S., 2003, Improbable areas in the visual brain, Trends Neurosci. 26:23–26.

    Article  Google Scholar 

  • Zhou, H., Friedman, H. S., and von der Heydt, R., 2000, Coding of border ownership in monkey visual cortex, J. Neurosci. 20:6594–6611.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Kluwer Academic/Plenum Publishers

About this chapter

Cite this chapter

Finkel, L.H., Contreras, D. (2005). Mechanisms of Cortical Computation. In: He, B. (eds) Neural Engineering. Bioelectric Engineering. Springer, Boston, MA. https://doi.org/10.1007/0-306-48610-5_8

Download citation

  • DOI: https://doi.org/10.1007/0-306-48610-5_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-306-48609-8

  • Online ISBN: 978-0-306-48610-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics