Skip to main content

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 39))

  • 834 Accesses

Abstract

We developed a dynamical systems approach to spatio-temporal neurodynamics. The corresponding mathematical objects are called Freeman K sets, which are mesoscopic models representing an intermediate-level between microscopic neurons and macroscopic brain structures. K sets are multi-scale models, describing increasing complexity of structure and dynamical behaviors.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

References

  1. Freeman WJ (2001) How brains make up their minds. Columbia UP, New York

    Google Scholar 

  2. Katchalsky Katzir A (1971) Biological flow structures and their relation to chemodiffusional coupling. Neurosci Res Prog Bull 9:397–413

    Google Scholar 

  3. Freeman WJ, Erwin H (2008) Freeman K-set. Scholarpedia 3(2):3238

    Article  Google Scholar 

  4. Freeman WJ (2000/2006) Neurodynamics. An exploration of mesoscopic brain dynamics. Springer, London. Electronic version, http://sulcus.berkeley.edu/

    Google Scholar 

  5. Freeman WJ, Chang H-J, Burke BC, Rose PA, Badler J (1997) Taming chaos: stabilization of aperiodic attractors by noise. IEEE Trans Circuits Syst 44:989–996

    Article  MathSciNet  Google Scholar 

  6. Kozma R, Freeman WJ (2001) Chaotic resonance: methods and applications for robust classification of noisy and variable patterns. Int J Bifurc Chaos 10:2307–2322

    Google Scholar 

  7. Xu D, Principe JC (2004) Dynamical analysis of neural oscillators in an olfactory cortex model. IEEE Trans Neural Netw 15(5):1053–1062

    Article  Google Scholar 

  8. Ilin R, Kozma R (2006) Stability of coupled excitatory-inhibitory neural populations application to control multistable systems. Phys Lett A 360:66–83

    Article  Google Scholar 

  9. Gutierrez-Galvez A, Gutierrez-Osuna R (2006) Increasing the separability of chemo-sensor array patterns with Hebbian/anti-Hebbian learning. Sens Actuators B: Chem 116(1):29–35

    Article  Google Scholar 

  10. Chang HJ, Freeman WJ (1996) Parameter optimization in models of the olfactory neural system. Neural Netw 9(1):1–14

    Article  Google Scholar 

  11. Chang HJ, Freeman WJ, Burke BC (1998) Optimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors. Neural Netw 11(3):449–466

    Article  Google Scholar 

  12. Kozma R, Freeman WJ (2002) Classification of EEG patterns using nonlinear neurodynamics and chaos. Neurocomputing 44–46:1107–1112

    Article  MATH  Google Scholar 

  13. Beliaev I, Kozma R (2007) Time series prediction using chaotic neural networks on the CATS benchmark. Neurocomputing 70(13):2426–2439

    Article  Google Scholar 

  14. Kozma R (2007) Neuropercolation. Scholarpedia 2(8):1360

    Article  Google Scholar 

  15. Harter D, Kozma R (2005) Chaotic neurodynamics for autonomous agents. IEEE Trans Neural Netw 16(3):565–579

    Article  Google Scholar 

  16. Harter D, Kozma R (2006) Aperiodic dynamics and the self-organization of cognitive maps in autonomous agents. Int J Intell Syst 21(9):955–972

    Article  MATH  Google Scholar 

  17. Kozma R, Freeman WJ (2003) Basic principles of the KIV model and its application to the navigation problem. J Integr Neurosci 2(1):125–146

    Article  Google Scholar 

  18. Kozma R, Freeman WJ, Erdi P (2003) The KIV model D nonlinear spatio-temporal dynamics of the primordial vertebrate forebrain. Neurocomputing 52–54:819–826

    Article  Google Scholar 

  19. Kozma R, Freeman WJ (2009) The KIV model of intentional dynamics and decision making. Neural Netw 22(3):277–285

    Article  Google Scholar 

  20. Huntsberger T, Tunstel E, Aghazarian H, Kozma R (2006) Onboard learning strategies for planetary surface rovers. Intell Space Robot 403–422

    Google Scholar 

  21. Kozma R, Huntsberger T, Aghazarian H, Tunstel E, Ilin R, Freeman WJ (2008) Intentional control for planetary rover SRR2K. Adv Robot 21(8):1109–1127

    Google Scholar 

  22. James W (1893) The principles of psychology. H. Holt, New York

    Google Scholar 

  23. Kandel ER, Schwartz JH, Jessell TM (2000) Principles of neuroscience. McGraw Hill, New York

    Google Scholar 

  24. Dayan P, Abbott LF (2001) Theoretical neuroscience. MIT Press, Cambridge

    MATH  Google Scholar 

  25. Izhikevich EM (2006) Dynamical systems in neuroscience–the geometry of excitability and bursting. MIT Press, Cambridge

    Google Scholar 

  26. Freeman WJ (1975/2004) Mass action in the nervous system. Academic, New York. Electronic version 2004. http://sulcus.berkeley.edu/MANSWWW/MANSWWW.html

    Google Scholar 

  27. Freeman WJ (1967) Analysis of function of cerebral cortex by use of control systems theory. Logist Rev 3:5–40

    Google Scholar 

  28. Gray CM, Skinner JE (1988) Centrifugal regulation of neuronal activity in the olfactory bulb of the waking rabbit as revealed by reversible cryogenic blockade. Exp Brain Res 69:378–86

    Article  Google Scholar 

  29. Freeman WJ (1979) Nonlinear dynamics of paleocortex manifested in the olfactory EEG. Biol Cybern 35:21–37

    Article  Google Scholar 

  30. Emery JD, Freeman WJ (1969) Pattern analysis of cortical evoked potential parameters during attention changes. Physiol Behav 4:67–77

    Article  Google Scholar 

  31. Abeles M (1991) Corticonics: neural circuits of the cerebral cortex. Cambridge UP, New York

    Book  Google Scholar 

  32. Aihara K, Takabe T, Toyoda M (1990) Chaotic neural network. Phys Lett A 144:333–340

    Article  MathSciNet  Google Scholar 

  33. Anderson JA, Silverstein JW, Ritz SR, Jones RS (1977) Distinctive features, categorical perception, and probability learning: some applications of a neural model. Psychol Rev 84:413–451

    Article  Google Scholar 

  34. Hopfield JJ (1982) Neuronal networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 81:3058–3092

    Google Scholar 

  35. Kohonen T (2001) Self-organizing maps. Springer, Berlin

    Book  MATH  Google Scholar 

  36. Freeman WJ (2005) Origin, structure, and role of background EEG activity. Part 3. Neural frame classification. Clin Neurophysiol 116(5):1118–1129

    Article  Google Scholar 

  37. Makowiec D, Gnacinski P (2002) Universality class of probabilistic cellular automata. Cellular automata. Springer, Berlin, pp 104–113

    Chapter  Google Scholar 

  38. Turova TS (2012) The emergence of connectivity in neuronal networks: from bootstrap percolation to auto-associative memory. Brain Res 1434:277–284

    Article  Google Scholar 

  39. Regnault D (2013) Proof of a phase transition in probabilistic cellular automata. Developments in language theory. Springer, Berlin, pp 433–444

    Chapter  Google Scholar 

  40. Turova T, Vallier T (2015) Bootstrap percolation on a graph with random and local connections. arXiv preprint arXiv:1502.01490

  41. Kadanoff LP, Ceva H (1971) Determination of an operator algebra for a two-dimensional Ising model. Phys Rev B3:3918

    Article  MathSciNet  Google Scholar 

  42. Odor G (2004) Universality classes in nonequilibrium lattice systems. Rev Mod Phys 76:663–724

    Article  MathSciNet  MATH  Google Scholar 

  43. Makowiec D (1999) Stationary states for Toom cellular automata in simulations. Phys Rev E 60:3787–3796

    Article  Google Scholar 

  44. Binder K (1981) Finite scale scaling analysis of Ising model block distribution function. Z Phys B 43:119–140

    Article  Google Scholar 

  45. Puljic M, Kozma R (2005) Activation clustering in neural and social networks. Complexity 10(4):42–50

    Article  MathSciNet  Google Scholar 

  46. Kozma R, Puljic M, Balister P, Bollobas B, Freeman WJ (2005) Phase transitions in the neuropercolation model of neural populations with mixed local and non-local interactions. Biol Cybern 92:367–379

    Article  MathSciNet  MATH  Google Scholar 

  47. Puljic M, Kozma R (2008) Narrow-band oscillations in probabilistic cellular automata. Phys Rev E 78026214

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Robert Kozma .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kozma, R., Freeman, W.J. (2016). Supplement I: Mathematical Framework. In: Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Studies in Systems, Decision and Control, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-24406-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24406-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24404-4

  • Online ISBN: 978-3-319-24406-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics