We report the existence of phase-coupled oscillations in a model neural system. The model consists of a group of excitatory principal cells in interaction with local inhibitory interneurons. The voltages across the membranes of excitatory cells are governed primarily by calcium and potassium ion conductivities. The number of potassium channels open at any given instant changes in accordance with a deterministic law. The time scale of this change is set by a constant which depends on midpoint potentials at which potassium and calcium currents are half-activated. The growth of mean membrane potential of excitatory principal cells is controlled by that of the inhibitory interneurons. Nonlinear oscillatory system associated with these limit cycles starting from two different initial conditions maintain a definite phase relationship. The phase-coupled oscillations in electrical activity of the neuronal cells carry together amplitude, phase, and time information for cellular signaling. This mechanism supports an energy efficient way of information processing in the central nervous system. The information content is encoded as persistent periodic oscillations represented by stable limit cycles in the phase space. 1. Introduction The rhythmic oscillations are abundant in nature and they also exist at all levels of organization in the biotic world. The well-known examples are circadian rhythms [1], relaxation oscillations in heart beats [2, 3], and gene regulatory networks [4]. The electrical and magnetic activity of human brain has many oscillatory networks that coexist, and they perform unique physiological and cognitive functions. The frequency of oscillations in these networks is used to categorize them in different bands such as , , , and [5], and they characterize different physiological states. The oscillations in band represent eyes closed/relaxed states, the band is characteristic of eyes open, cognition is and alert state, during higher cognitive functioning, and is during sleep condition or under the action of anesthesia. These oscillations represent ongoing and spontaneous activities of the brain as under process by different neuronal organizations emerging from arrays of neuronal tissues and cells in the brain. The neuronal cells communicate with each other through action potentials as stated in electrophysiological parlance. These action potentials are rapid reversals in voltages across the plasma membrane of neuronal cells axons, mediated by voltage-gated ion channels [6]. Brain oscillatory networks play a key role in information processing, and
References
[1]
S. Daan and C. Berde, “Two coupled oscillators: simulations of the circadian pacemaker in mammalian activity rhythms,” Journal of Theoretical Biology, vol. 70, no. 3, pp. 297–313, 1978.
[2]
B. Van der Pol and J. Van der Mark, “The heartbeat considered as a relaxation oscillation, and an electrical model of the heart,” Philosophical Magazine, vol. 6, pp. 763–775, 1928.
[3]
A. M. Dos Santos, S. R. Lopes, and R. L. Viana, “Rhythm synchronization and chaotic modulation of coupled Van der Pol oscillators in a model for the heartbeat,” Physica A, vol. 338, no. 3-4, pp. 335–355, 2004.
[4]
J. Hasty, D. McMillen, F. Isaacs, and J. J. Collins, “Computational studies of gene regulatory networks: in numero molecular biology,” Nature Reviews Genetics, vol. 2, no. 4, pp. 268–279, 2001.
[5]
G. Buzsáki and A. Draguhn, “Neuronal olscillations in cortical networks,” Science, vol. 304, no. 5679, pp. 1926–1929, 2004.
[6]
Z. Sands, A. Grottesi, and M. S. Sansom, “Voltage-gated ion channels,” Current Biology, vol. 15, no. 2, pp. R44–R47, 2005.
[7]
W. J. Freeman, “Tutorial on neurobiology: from single neurons to brain chaos,” International Journal of Bifurcation and Chaos, vol. 2, pp. 451–482, 1992.
[8]
A. L. Hodgkin and A. F. Huxley, “The components of membrane conductance in the giant axon of Loligo,” The Journal of Physiology, vol. 116, no. 4, pp. 473–496, 1952.
[9]
C. Morris and H. Lecar, “Voltage oscillations in the barnacle giant muscle fiber,” Biophysical Journal, vol. 35, no. 1, pp. 193–213, 1981.
[10]
S. R. Nadar and V. Rai, “Transient periodicity in a morris lecar neural system,” ISRN Biomathematics, vol. 2012, Article ID 546315, 7 pages, 2012.
[11]
G. A. Carpenter, “A geometric approach to singular perturbation problems with applications to nerve impulse equations,” Journal of Differential Equations, vol. 23, no. 3, pp. 335–367, 1977.
[12]
N. Fenichel, “Geometric singular perturbation theory for ordinary differential equations,” Journal of Differential Equations, vol. 31, no. 1, pp. 53–98, 1979.
[13]
E. M. Izhikevich, “Neural excitability, spiking and bursting,” International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, vol. 10, no. 6, pp. 1171–1266, 2000.
[14]
E. M. Izhikevich, Dynamical Systems in Neuroscience: the Geometry of Excitability and Bursting, MIT Press, Cambridge, Mass, USA, 2007.
[15]
M. Colombo and C. G. Gross, “Responses of inferior temporal cortex and hippocampal neurons during delayed matching to sample in monkeys (Macaca fascicularis),” Behavioral Neuroscience, vol. 108, no. 3, pp. 443–455, 1994.
[16]
N. Axmacher, C. E. Elger, and J. Fell, “Working memory-related hippocampal deactivation interferes with long-term memory formation,” Journal of Neuroscience, vol. 29, no. 4, pp. 1052–1060, 2009.
[17]
X. J. Wang, “Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory,” Journal of Neuroscience, vol. 19, no. 21, pp. 9587–9603, 1999.
[18]
S. Makeig, M. Westerfield, T. P. Jung et al., “Dynamic brain sources of visual evoked responses,” Science, vol. 295, no. 5555, pp. 690–694, 2002.
[19]
B. Ermentrout, “Type I membranes, phase resetting curves, and synchrony,” Neural Computation, vol. 8, no. 5, pp. 979–1001, 1996.
[20]
H. Y. Jeong and B. Gutkin, “Synchrony of neuronal oscillations controlled by GABAergic reversal potentials,” Neural Computation, vol. 19, no. 3, pp. 706–729, 2007.
[21]
F. Collette and M. Van Der Linden, “Brain imaging of the central executive component of working memory,” Neuroscience and Biobehavioral Reviews, vol. 26, no. 2, pp. 105–125, 2002.
[22]
A. D. Baddeley, “Short-term memory for word sequences as a function of acoustic, semantic and formal similarity,” The Quarterly Journal of Experimental Psychology, vol. 18, no. 4, pp. 362–365, 1966.
[23]
A. D. Baddeley, Working Memory, Oxford University Press, Oxford, UK, 1986.
[24]
P. Larimer and B. W. Strowbridge, “Representing information in cell assemblies: persistent activity mediated by semilunar granule cells,” Nature Neuroscience, vol. 13, no. 2, pp. 213–222, 2010.
[25]
D. D. Fraser and B. A. MacVicar, “Cholinergic-dependent plateau potential in hippocampal CA1 pyramidal neurons,” Journal of Neuroscience, vol. 16, no. 13, pp. 4113–4128, 1996.
[26]
A. Gupta, F. S. Elqammal, A. Proddutur, S. Shah, and V. Shantakumar, “Decrease in tonic inhibition contributes to increase in dentate semilunar granule cell excitability after brain injury,” Journal of Neuroscience, vol. 32, no. 7, pp. 2523–2537, 2012.