Methods Inf Med 1994; 33(01): 125-128
DOI: 10.1055/s-0038-1634984
Original Article
Schattauer GmbH

Pharmacological and Model-based Interpretation of Neuronal Dynamics Transitions during Sleep-Waking Cycle

M. Yamamoto
1   Laboratory of Neurophysiology and Bioinformatics, Graduate School of Information Sciences, Tohoku University, Japan
,
M. Nakao
1   Laboratory of Neurophysiology and Bioinformatics, Graduate School of Information Sciences, Tohoku University, Japan
,
Y. Mizutani
1   Laboratory of Neurophysiology and Bioinformatics, Graduate School of Information Sciences, Tohoku University, Japan
,
T. Takahashi
1   Laboratory of Neurophysiology and Bioinformatics, Graduate School of Information Sciences, Tohoku University, Japan
,
K. Watanabe
1   Laboratory of Neurophysiology and Bioinformatics, Graduate School of Information Sciences, Tohoku University, Japan
,
H. Arai
1   Laboratory of Neurophysiology and Bioinformatics, Graduate School of Information Sciences, Tohoku University, Japan
,
N. Sasaki
1   Laboratory of Neurophysiology and Bioinformatics, Graduate School of Information Sciences, Tohoku University, Japan
› Author Affiliations
Further Information

Publication History

Publication Date:
08 February 2018 (online)

Abstract:

Power spectral analysis has been applied to spontaneous single neuronal activities during the sleep-waking cycle in various regions of the cat’s central nervous system. During slow-wave sleep (SWS), the spontaneous activities of many neurons had a white noise-like power-spectral density profile in a very low frequency range (0.01-1.0 Hz) whereas, during rapid-eye-movement sleep (REMS), they showed a 1/f-like spectral pattern. This spectral transition between SWS and REMS was hypothesized to depend on the influence of serotonergic and cholinergic neuronal activity which is considered to modulate various brain functions. According to both pharmacological experiments and simulation studies with a neural network model, it was concluded that the serotonergic system may have a function to eliminate slow fluctuations in neuronal activity in wide areas, from the reticulothalamo-neocortical to the limbic systems. Consequently, simple signal processing of spontaneous neuronal activity has elucidated an important neurophysiological fact, which may lead to a principle of the basic brain function and its mechanism.

 
  • REFERENCES

  • 1 Yamamoto M, Nakahama H, Shima K, Kodama T, Mushiake G. Markov-dependency and spectral analyses on spike-counts in mesencephalic reticular neurons during sleep and attentive states. Brain Res 1986; 366: 279-89.
  • 2 Yamamoto M, Nakahama H, Shima K, Aya K, Kodama T. Neuronal activities during paradoxical sleep. Adv Neurol Sci 1986; 30: 1010-22.
  • 3 Kodama T, Mushiake H, Shima K, Nakahama H, Yamamoto M. Slow fluctuations of single unit activities of hippocampal and thalamic neurons in cats. I. Relation to natural sleep and alert states. Brain Res 1989; 487: 26-34.
  • 4 Mushiake H, Kodama T, Shima K, Yamamoto M, Nakahama H. Fluctuations in spontaneous discharge of hippocampal theta cells during sleep-waking states and PCPA-induced insomnia. J Neurophysiol 1988; 60: 925-39.
  • 5 McGinty D, Harper RM. Dorsal raphe neurons: depression of firing during sleep in cats. Brain Res 1976; 101: 569-75.
  • 6 Shima K, Nahakama H, Yamamoto M. Firing properties of two types of nucleus raphe dor-salis neurons during the sleep-waking cycle and their responses to sensory stimuli. Brain Res 1986; 399: 317-26.
  • 7 Kodama T, Takahashi T, Honda Y. Enhancement of acetylcholine release during paradoxical sleep in the dorsal segmental field of the cat brainstem. Neurosci Lett 1990; 114: 227-82.
  • 8 Kodama T, Mushiake H, Shima K, Hayashi T, Yamamoto M. Slow fluctuations of single unit activities of hippocampal and thalamic neurons in cats. II. Roles of serotonin on the stability of neuronal activities. Brain Res 1989; 487: 35-44.
  • 9 Nakao M, Takahashi T, Mizutani Y, Yamamoto M. Simulation study on dynamics transition in neuronal activity during sleep cycle by using asynchronous and symmetry neural network model. Biol Cybern 1990; 63: 243-50.