Skip to main content
SpringerLink
Log in

The Development of Cortical Models to Enable Neural-based Cognitive Architectures

  • Chapter
Computational Models for Neuroscience

  • 247 Accesses

Abstract

The development of models of the cerebral cortex parallels the growth in sophistication of neural models in general, proceeding from heuristic models, to functional “black box” systems approaches, to large-scale neural circuit models. Currently, computational neuroscience is producing increasingly detailed neurobiologically based models of cortex and related structures. Many of these are intended as simulations of the biology, and are subject only to the constraint of predicting experimental observations in the neurobiological domain. A few of these models have some computational capability in the general domains of pattern recognition or control (Ambros-Ingerson et al., 1990; Carpenter and Grossberg, 1991; McKenna, 1994; Zornetzer et al., 1995).

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 EPUB and 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. (1991) Corticonics. Cambridge, England: Cambridge University Press.

    Book  Google Scholar 

  • Abeles, M., Vaadia, E., Bergman, H., Prut, Y., Haalman, I., Slovin, H. (1993) Dynamics of neuronal interactions in the frontal cortex of behaving monkeys. Concepts in Neuroscience 4: 131–158.

    Google Scholar 

  • Aleksandrovsky, B., Whitson, J., Andes, G., Lynch, G., Granger, R. (1996a) Novel speech processing mechanism derived from auditory neocortical circuit analysis. Proc. ICSLP Int’l. Conf. On Spoken Language Proc. IEEE Press 1: 558–561.

    Google Scholar 

  • Aleksandrovsky, B., Whitson, J., Garzotto, A., Lynch, G., Granger, R. (1996b) An algorithm derived from thalamocortical circuitry stores and retrieves temporal sequences. Proc. Int’l. Conf. Pattern Recog., IEEE Comp. Soc. Press 4: 550–554.

    Google Scholar 

  • Aleksandrovsky, B., Brucher, F., Lynch, G. (1997a) Neural network model of striatal complex. In: Biological and Artificial Computation: From Neuroscience to Technology. IWANN’97 International Conference on Artificial and Natural Neural Networks, Lecture Notes in Computer Science 1240. Berlin: Springer-Verlag, pp. 103–115.

    Chapter  Google Scholar 

  • Aleksandrovsky, B., Whitson, J., Garzotto, A., Lynch, G., Granger, R. (1997b) A continuous temporal sequence recognition device based on a model of structure and function in the neocortex. Technical Report, Brain Theory Project, University of California, Irvine.

    Google Scholar 

  • Alexander, G.E., Crutcher, M.D. (1990) Functional architecture of basal ganglia circuits: Neural substrates of parallel processing. Trends in Neurosciences 13: 266–271.

    Article  Google Scholar 

  • Ambros-Ingerson, J., Granger, R., Lynch, G. (1990) Simulation of paleocortex performs hierarchical clustering. Science 247: 1344–1348.

    Article  Google Scholar 

  • Anderson, J.R. (1993) Rules of the Mind. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Anderson, J.R., Matessa, M., Labiere, C. (1997) ACT-R: A theory of higher level cognition and its relation to visual attention. Human Computer Interaction 12: 439–462.

    Article  Google Scholar 

  • Angeline, P., Saunders, G., Pollack, J. (1994) An evolutionary algorithm that constructs recurrent networks. IEEE Trans. on Neural Networks 5: 54–65.

    Article  Google Scholar 

  • Anton, P., Lynch, G., Granger, R. (1991) Computation of frequency-to-spatial transform by olfactory bulb glomeruli. Biol. Cybern. 65: 407–414.

    Article  Google Scholar 

  • Bachmann, C.M., Musman, S., Luong, D., Schultz, A. (1994) Unsupervised BCM projection pursuit algorithms for classification of simulated radar presentations. Neural Networks 7: 709–728.

    Article  Google Scholar 

  • Bowers, J.M., Beeman, D. (Eds.) (1998) The Book of Genesis: Exploring Realistic Neural Models with the General Neural Simulation System. 2nd ed. New York: Springer-Verlag.

    Google Scholar 

  • Braitenberg, V., Schuz, A. (1991) Anatomy of the Cortex: Statistics and Geometry. Berlin: Springer-Verlag.

    Google Scholar 

  • Braver, T.S., Cohen, J.D. (1999) On the control of control: The role of dopamine in regulating prefrontal function and working memory. In: S. Monsell, J. Driver (Eds.) Attention and Performance XVII. Cambridge, MA: MIT Press.

    Google Scholar 

  • Braver, T.S., Cohen, J.D. (2001) Working memory, cognitive control, and the prefrontal cortex: computational and empirical studies. Cognitive Processing 2: 25–55.

    Google Scholar 

  • Brooks, R., Breazeal, C., Marjanović, M., Scassellati, B., Williamson, M. (1999) The Cog project: Building a humanoid robot. In: C. Nehaniv (Ed.) Computation for Metaphors, Analogy, and Agents, Lecture Notes in Artificial Intelligence 1562. New York: Springer-Verlag, pp. 52–87.

    Chapter  Google Scholar 

  • Bullock, D., Cisek, P.E., Grossberg, S. (1998) Cortical networks for control of voluntary arm movements under variable force conditions. Cerebral Cortex 8: 48–62.

    Article  Google Scholar 

  • Bullock, D., Fiala, J.C., Grossberg, S. (1994) A neural model of timed response learning in the cerebellum. Neural Networks 7: 1101–1114.

    Article  MATH  Google Scholar 

  • Carpenter, G.A., Grossberg, S. (Eds.) (1991) Pattern Recognition by Self-Organizing Neural Networks. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Carpenter, P.A., Just, M.A., Shell, P. (1990) What one intelligence test measures: A theoretical account of the Raven Progressive Matrices Test. Psychological Review 97: 404–431.

    Article  Google Scholar 

  • Chandrasekaran, A. (1994) Architecture of Intelligence: The problems and current approaches to solutions. In: V. Honavar, L. Uhr (Eds.) Artificial Intelligence and Neural Networks. New York: Academic Press.

    Google Scholar 

  • Chipman, S.F. (1992) The higher-order cognitive skills: What they are and how they might be transmitted. In: T.G. Sticht, B.A. McDonald, M.J. Beeler (Eds.) Intergenerational Transfer of Cognitive Skills: Vol. II: Theory and Research in Cognitive Science. Norwood, NJ: Ablex, pp. 128–158.

    Google Scholar 

  • Cohen, J.D., Braver, T.S., O’Reilly, R.C. (1996) A computational approach to prefrontal cortex, cognitive control and schizophrenia: recent developments and current challenges. Phil. Trans. R. Soc. Lond. B. 351: 1515–1527.

    Article  Google Scholar 

  • Cohen, J.D., Dunbar, K., McClelland, J.L. (1990) On the control of automatic processes: A parallel distributed processing model of the stroop effect. Psychological Review 97: 332–361.

    Article  Google Scholar 

  • Contreras-Vidal, J.L., Grossberg, S., Bullock, D. (1997) A neural model of cerebellar learning for arm movement control: Cortico-spinal-cerebellar dynamics. Learning and Memory 3: 475–502.

    Article  Google Scholar 

  • Cooper, R., Shallice, T. (1995) Soar and the case for unified theories of cognition.Cognition 55: 115–149.

    Article  Google Scholar 

  • Coultrip, R., Granger, R., Lynch, G. (1992) A cortical model of winner-take-all competition via lateral inhibition. Neural Networks 5: 47–54.

    Article  Google Scholar 

  • Cox, A.L. and Young, R.M. (2000) Device-oriented and task oriented exploratory learning of interface designs. In: Proceedings of the Third International Conference on Cognitive Modeling. Veenendaal, Netherlands: Universal Press, pp. 70–77.

    Google Scholar 

  • Deadwyler, S.A., Hampson, R.E. (1997) The significance of neural ensemble codes during behavior and cognition. Ann. Rev. Neurosci. 20: 217–244.

    Article  Google Scholar 

  • DeFelipe, J., Jones, E.G. (1988) Cajal on the Cerebral Cortex: An annotated translation of the complete writings. New York: Oxford University Press.

    Google Scholar 

  • Dehaene, S., Changeux, J.P. (1992) The Wisconsin card sorting test: Theoretical analysis and modeling in a neuronal network. Cerebral Cortex 1: 62–79.

    Article  Google Scholar 

  • Douglas, R.J., Koch, C., Mahowald, M., Martin, K., Suarez, H. (1995) Recurrent excitation in neocortical circuits. Science 269: 981–985.

    Article  Google Scholar 

  • Douglas, R.J., Martin, K.A.C. (1990) Neocortex. In: G.M. Sheperd (Ed.) The Synaptic Organization of the Brain, 3rd Ed. New York: Oxford University Press, pp. 389–438.

    Google Scholar 

  • Favorov, O.V., Hester, J.T., Kelly, D.G, Tommerdahl, M., Whitsel, B.L. (1998) Lateral interactions in cortical networks. In: M.J. Rowe (Ed.) Somatosensory Processing: From Single Neuron to Brain Imaging. Langhorne, PA: Harwood, pp.187–207.

    Google Scholar 

  • Feldman, J.A. (1991) Cognition as search. Science 251: 575.

    Article  Google Scholar 

  • Ferrell, C. (1996) Orientation behavior using registered topographic maps. In: From Animals to Animats, Proc. 1996 meeting of Soc. of Adaptive Behavior. Cape Cod, MA, pp. 94–103.

    Google Scholar 

  • Frank, M., Loughry, B., O’Reilly, R.C. (2001) Interactions between the frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, and Behavioral Neuroscience 1: 137–160.

    Article  Google Scholar 

  • Freeman, W.J. (1992) Tutorial in neurobiology: From single neurons to brain chaos. Int. J. Bifurcation and Chaos 2: 451–482.

    Article  MATH  Google Scholar 

  • Gancarz, G., Grossberg, S. (1999) A neural model of saccadic eye movement control explains task-specific adaptation. Vision Res. 39: 3123–3143.

    Article  Google Scholar 

  • Garzotto, A., Aleksandrovsky, B., Lynch, G., Granger, R. (1997) A neocortically derived model of continuous contextual processing. Proc. International Conference on Neural Networks, IEEE Press 1: 564–568.

    Google Scholar 

  • Gluck, K.A. (2000) An ACT-R/PM model of algebra symbolization. In: N. Taatgen, J. Aasman (Eds.) Proceedings of the Third International Conference on Cognitive Modeling. Veenendaal, Netherlands: Universal Press, pp. 134–141.

    Google Scholar 

  • Gluck, M.A., Granger, R. (1993) Computational aspects of the neural bases of learning and memory. Annual Review of Neuroscience 16: 667–706.

    Article  Google Scholar 

  • Gluck, M.A., Myers, C.E. (1997) Psychological models of hippocampal function in learning and memory. Annual Review of Psychology 48: 481–514.

    Article  Google Scholar 

  • Granger, R., Ambros-Ingerson, J., Lynch, G. (1989) Derivation of encoding characteristics of layer II cerebral cortex. J. Cognit. Neurosci. 1: 61–87.

    Article  Google Scholar 

  • Granger, R., Cobas, A., Lynch, G. (1991a) Possible computations of primary sensory cortex: Hypotheses based on computer models of olfaction and audition. In: M. Baudry, J. Davis (Eds.) Current Issues in LTP. Cambridge, MA: MIT Press.

    Google Scholar 

  • Granger, R., Staubil, U., Powers, H., Otto, T., Ambros, J., Lynch, G. (1991b) Behavioral tests of a prediction from a cortical network simulation. Psychological Science 2: 116–118.

    Article  Google Scholar 

  • Granger, R., Wiebe, S.P., Taketani, Lynch, G. (1996) Distinct memory circuits composing the hippocampal region. Hippocampus 6: 567–578.

    Article  Google Scholar 

  • Grossberg, S. (1997) Cortical dynamics of three-dimensional figure-ground perception of two-dimensional pictures. Psychol. Rev. 104: 618–658.

    Article  Google Scholar 

  • Grossberg, S., Mingolla, E., Ross, W.D. (1997) Visual brain and visual perception: how does the cortex do perceptual grouping? Trends in Neurosciences 2: 106–111.

    Article  Google Scholar 

  • Gunzelmann, G., Anderson, J.R. (2001) Modeling the emergence of strategies and their effects on problem difficulty in ACT-R. In: Proceedings of the Fourth International Conference on Cognitive Modeling. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 109–114.

    Google Scholar 

  • Gupta, M.M., Knopf, G.K. (1994) Neuro-Vision Systems: Principles and Applications. New York: IEEE Press.

    MATH  Google Scholar 

  • Hecht-Nielsen, R. (1998) A theory of the cerebral cortex. Proc. 1998 Int’l. Conf. on Neural Information Processing. Kitakyushu, Japan, pp. 1459–1464.

    Google Scholar 

  • Iatrou, M., Berger, T.W., Marmarelis, V.Z. (1999) Application of novel modeling method to the nonstationary properties of potentiation in the rabbit hippocampus. Annals of Biomedical Engineering 27: 581–591.

    Article  Google Scholar 

  • Just, M.A., Carpenter, P.A. (1985) Cognitive coordinate systems: Accounts of mental rotation and individual differences in spatial ability. Psychological Review 92: 137–172.

    Article  Google Scholar 

  • Just, M.A., Carpenter, P.A. (1992) A capacity theory of comprehension: Individual differences in working memory. Psychological Review 99: 122–149.

    Article  Google Scholar 

  • Just, M.A., Carpenter, P.A., Hemphill, D.D. (1996) Constraints on processing capacity: Architectural or implementational? In: D. Steier, T. Mitchell (Eds.) Mind Matters: A Tribute to Allan Newell. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Just, M.A., Carpenter, P.A., Shell, P. (1990) What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review 97: 404–431.

    Article  Google Scholar 

  • Just, M.A., Carpenter, P.A., Varma, S. (1999) Computational modeling of high-level cognition and brain function. Human Brain Mapping 8: 128–136.

    Article  Google Scholar 

  • Kilborn, K., Kubota, D., Lynch, G., Granger, R. (1998) Parameters of LTP induction modulate network categorization behavior. In: J.M. Bowers (Ed.) Computational Neuroscience: Trends in Research. New York: Plenum Press, pp. 353–358.

    Google Scholar 

  • Koch, C. (1999) Biophysics of Computation: Information Processing in Single Neurons. New York: Oxford University Press.

    Google Scholar 

  • Labiere, C. (1998) The dynamics of cognition: An ACT-R model of cognitive arithmetic. PhD Dissertation. CMU Computer Science Dept. Technical Report CMU-CS-98-186. Pittsburgh, PA. (http://reports-archive.adm.cs.cmu.edu/csl998.html)

    Google Scholar 

  • Laird, J.E., Newell, A., Rosenbloom, P.S. (1987) SOAR: An architecture for general intelligence. Artificial Intelligence 33(1): 1–63.

    Article  MathSciNet  Google Scholar 

  • Landy, M.S., Movshon, J.A. (1991) Computational Models of Visual Processing. Cambridge, MA: MIT Press.

    Google Scholar 

  • Lund, J.S., Yoshioka, T., Levitt, J.B. (1994) Substrates for interlaminar connections in area V1 of macaque monkey cerebral cortex. In: A.A. Peters, K.S. Rockland (Eds.) Cerebral Cortex v. 10. New York: Plenum Press.

    Google Scholar 

  • Lyon, R., Shamma, S. (1996) Auditory representations of timbre and pitch. In: H.L. Hawkins, T.A. McMullen, A.N. Popper, R.R. Fay (Eds.) Auditory Computation. Berlin: Springer-Verlag, pp. 221–270.

    Chapter  Google Scholar 

  • Martin, K.A.C. (1988) The Wellcome Prize lecture: from single cells to simple circuits in the cerebral cortex. Quart. J. Exp. Physiol. 73: 637–702.

    Google Scholar 

  • McKenna, T.M. (1994) The role of interdisciplinary research involving neuroscience in the development of intelligent systems. In: V. Honavar, L. Uhr (Eds.) Artificial Intelligence and Neural Networks. New York: Academic Press.

    Google Scholar 

  • McKenna, T., Davis, J., Zornetzer, S.F. (1992) Single Neuron Computation. Boston: Academic Press.

    MATH  Google Scholar 

  • McKenna, T.M., McMullen, T.A., Shlesinger, M.F. (1994) The brain as a dynamic physical system. Neuroscience 60: 587–605.

    Article  Google Scholar 

  • Meyer, D.E., Kieras, D.E. (1997) A computational theory of executive cognitive processes and human multiple-task performance: Part 1. Basic mechanisms. Psychological Review 104: 3–65.

    Article  Google Scholar 

  • Mitani, A., Shimokouchi, M., Itoh, K., Nomura, S., Kudo, M. Mizuno, N. (1985) Morphology and laminar organization of electrophysiologically identified neurons in the primary auditory cortex. J. Comp. Neurol. 235: 430–447.

    Article  Google Scholar 

  • Newell, A. (1992) Unified theories of cognition and the role of Soar. In: J.A. Michon, A. Akyürek (Eds.) SOAR: A Cognitive Architecture in Perspective. Dordrecht, The Netherlands: Kluwer Academic Publishers, pp. 25–79.

    Chapter  Google Scholar 

  • Nicolelis, M.A.L., Baccala, L.A., Lin, R.C.S., Chapin, J.K. (1995) Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system. Science 268: 1353–1358.

    Article  Google Scholar 

  • Nicolelis, M.A.L., Fanselow, E.E., Ghazanfar, A.A. (1997) Hebb’s dream: The resurgence of cell assemblies. Neuron 19: 219–221.

    Article  Google Scholar 

  • Nicolelis, M.A.L., Fanselow, E.E., Shuler, M. Henriquez, C. (2001) A critique of the pure feedforward model of touch. In: R.J. Nelson (Ed.) The Somatosensory System: Deciphering the Brain’s Own Body System. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Nicolelis, M.A.L., Katz, D., Krupa, D.J. (1998) Potential circuit mechanisms underlying concurrent thalamic and cortical plasticity. Rev. Neurosci. 9: 213–224.

    Article  Google Scholar 

  • O’Reilly, R.C. (1996) Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural Comput. 8: 895–938.

    Article  Google Scholar 

  • O’Reilly, R.C. (1998) Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences 2: 455–462.

    Article  Google Scholar 

  • O’Reilly, R.C. (2001) Generalization in interactive networks: The benefits of inhibitory competition and Hebbian learning. Neural Computation 13: 1199–1241.

    Article  MATH  Google Scholar 

  • O’Reilly, R.C., Braver, T.S., Cohen, J.D. (1999). A biologically-based computational model of working memory. In: A. Miyake, P. Shah (Eds.) Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York: Cambridge University Press, pp. 375–411.

    Google Scholar 

  • O’Reilly, R.C., Munakata, Y. (2000) Computational Explorations in Cognitive Neuro-science: Understanding of the Mind by Simulating the Brain. Cambridge, MA: MIT Press.

    Google Scholar 

  • O’Reilly, R.C., Noelle, D.C., Braver, T.S., Cohen, J.D. (2002) Prefrontal cortex in dynamic categorization tasks: Representational organization and neuromodulatory control. Cerebral Cortex 12: 246–257.

    Article  Google Scholar 

  • O’Reilly, R.C., Rudy, J.W. (2001) Conjunctive representations in learning and memory: Principles of cortical and hippocampal function. Psychological Review 108: 311–345.

    Article  Google Scholar 

  • Pearce, T.C, Vershure, P.F.M.J., White, J., Kauer, J.S. (2001) Stimulus encoding during the early stages of olfactory processing: A modeling study using an artificial olfactory system. Neurocomputing 38: 299–306.

    Article  Google Scholar 

  • Port, R.F., van Gelder, T. (1995) Mind as Motion: Explorations in the Dynamics of Cognition. Cambridge, MA: MIT Press.

    Google Scholar 

  • Rosenbloom, P.S., Laird, J.E., Newell, A. (1993) The Soar Papers: Readings on Integrated Intelligence. Cambridge, MA: MIT Press.

    Google Scholar 

  • Salvucci, D.D., Boer, E.R., Liu, A. (2002) Toward an integrated model of driver behavior in a cognitive architecture. Transportation Research Record (in press).

    Google Scholar 

  • Scassellati, B. (1998) A binocular, foveated active vision system. Technical Report, Memo 1628, MIT Artificial Intelligence Lab.

    Google Scholar 

  • Schoenbaum, G., Eichenbaum, H. (1995) Information coding in the rodent prefrontal cortex. II. Ensemble activity in the orbitofrontal cortex. J. Neurophysiol. 70: 28–36.

    Google Scholar 

  • Schunn, C., Anderson, J.R. (1998) Scientific discovery. In: J.R. Anderson, C. Labiere (Eds.) The Atomic Components of Thought. Mahwah, NJ: Erlbaum, pp. 255–296.

    Google Scholar 

  • Schunn, C., Harrison, A. (2001) ACT-RS: A neuropsychologically inspired model for spatial reasoning. In: Proceedings of the Fourth International Conference on Cognitive Modeling. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 267–268.

    Google Scholar 

  • Schwartz, E.L., Greve, D.N., Bonmassar, G. (1995) Space-variant active vision: Definition, overview and examples. Neural Networks 8: 1297–1308.

    Article  Google Scholar 

  • Segev, I., Rinzel, J., Sheperd, G. (1995) The Theoretical Foundation of Dendritic Function: Selected Papers of Wilfred Rall with Commentaries. Cambridge, MA: MIT Press.

    Google Scholar 

  • Shamma, S. (1997) Auditory cortical representation of complex acoustic spectra as inferred from the ripple analysis method. Network: Computation in Neural Systems 7: 439–476.

    Article  Google Scholar 

  • Shastri, L., Ajjanagadde, V. (1993) From simple associations to systematic reasoning: a connectionist representation of rules, variable, and dynamic bindings using temporal synchrony. Behav. Brain Sci. 16: 417–494.

    Article  Google Scholar 

  • Singer, W. (1994) Putative functions of temporal correlations in neocortical processing. In: C. Koch, J. Davis (Eds.) Large-Scale Neuronal Theories of the Brain. Cambridge, MA: MIT Press, pp. 201–237.

    Google Scholar 

  • Snow, R.E., Kyllonen, P.C., Marshalek, B. (1984) The topography of ability and learning correlations. In: R.J. Sternberg (Ed.) Advances in the Psychology of Human Intelligence, Vol. 2. Hillsdale, NJ: Erlbaum, pp. 47–103.

    Google Scholar 

  • Sohn, M.-H., Ursu, S., Anderson, J.R., Stenger, V.A., Carter, C.S. (2000) The role of prefrontal cortex and posterior parietal cortex in task-switching. Proceedings of National Academy of Science, 13448-13453.

    Google Scholar 

  • Thelen, E., Smith, L.B. (1994) A Dynamic Systems Approach to the Development of Cognition and Action. Cambridge, MA: MIT Press.

    Google Scholar 

  • Thibadeau, R., Just, M.A., Carpenter, P.A. (1982) A model of the time course and content of reading. Cognitive Science 6: 157–203.

    Article  Google Scholar 

  • Thomson, A.M., Deuchars, J. (1994) Temporal and spatial properties of local circuits inneocortex. Trends in Neurosciences 17: 119–126.

    Article  Google Scholar 

  • Vincente, K.J., Kirklik, A. (1992) On putting the cart before the horse: Taking perception seriously in unified theories of cognition. Behavioral and Brain Sciences 15: 461–162.

    Article  Google Scholar 

  • Wang, K., Shamma, S. (1995) Representation of acoustic signals in the primary auditory cortex. IEEE Trans. Audio and Speech Proc. V3(5): 382–395.

    Article  Google Scholar 

  • Weinberger, N.M. (1995) Dynamic regulation of receptive fields and maps in the adult sensory cortex. Ann. Rev. Neurosci. 18: 129–158.

    Article  Google Scholar 

  • Weinberger, N.M., Ashe, J.H., Metherate, R., McKenna, T.M., Diamond, D.M., Bakin, J.S., Lennartz, R.C., Cassady, J.M. (1990) Neural adaptive information processing: A preliminary model of receptive-field plasticity in auditory cortex during Pavlovian conditioning. In: M. Gabriel, J. Moore (Eds.) Learning and Computational Neuroscience: Foundations of Adaptive Networks. Cambridge, MA: MIT Press, pp. 91–138.

    Google Scholar 

  • White, J., Dickinson, T.A., Walt, D.R., Kauer, J.S. (1998) An olfactory neuronal network for vapor recognition in an artificial nose. Biological Cybernetics 78: 245–251.

    Article  MATH  Google Scholar 

  • White, J., Kauer, J.S. (1999) Odor recognition in an artificial nose by spatio-temporal processing using an olfactory neuronal network. Neurocomputing 26: 919–924.

    Article  Google Scholar 

  • Wickens, J. (1997) Basal ganglia: Structure and computations. Network: Computation in Neural Systems 8: 77–109.

    Article  Google Scholar 

  • Wills, H.R., Kellogg, M.M., Goodman, P.H. (1999) A biologically realistic computer of neocortical associative learning for the study of aging and dementia. J. Invest. Med.47(2): 11A

    Google Scholar 

  • Zachary, W., Ryder, J., Hicinbothom, J. (1998) Cognitive task analysis and modeling of decision making in complex environments. In: J. Cannon-Bowers, E. Salas (Eds.) Decision Making Under Stress: Implications for Training and Simulation. Washington, DC: American Psychological Association Press.

    Google Scholar 

  • Zornetzer, S., Davis, J., Lau, C., McKenna, T. (Eds.) (1995) An Introduction to Neural and Electronic Networks (2nd Ed.). San Diego: Academic Press.

    Google Scholar 

Download references

Authors
  1. Thomas McKenna
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of California, San Diego, CA, USA

    Robert Hecht-Nielsen PhD

  2. HNC Software Inc, San Diego, CA, USA

    Robert Hecht-Nielsen PhD

  3. Office of Naval Research, Arlington, VA, USA

    Thomas McKenna PhD

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag London Limited

About this chapter

Cite this chapter

McKenna, T. (2003). The Development of Cortical Models to Enable Neural-based Cognitive Architectures. In: Hecht-Nielsen, R., McKenna, T. (eds) Computational Models for Neuroscience. Springer, London. https://doi.org/10.1007/978-1-4471-0085-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-0085-0_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-593-9

  • Online ISBN: 978-1-4471-0085-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation