Yoshinari Mizutani

Spectral distortion properties of the integral pulse frequency modulation model

The integral pulse frequency modulation (IPFM) model has been used for the following two purposes. First, it has been utilized to verify the correspondence between the spectral structure of autonomic input and the estimated spectrum of heart rate variability (HRV), relying mainly on the theoretical work of Bayly (1968). Second, the IPFM model provides a framework for evaluating how precisely the proposed method of HRV analysis could estimate the input spectral structure. However, the appropriateness of the IPFM model for both purposes has not been examined sufficiently in realistic situations. Here, the spectral structure of the pulse train generated by the IPFM model is theoretically derived for an input signal containing multiple frequency components. This is a more general condition than the single sinusoidal input signal used earlier. In accordance with the theoretical results, the magnitude of the spectral distortion is computed for a pair of varied frequencies, considering the corresponding coefficient of variation of interpulse intervals. Results show that the distortion could be nonnegligible under practical values of the coefficient of variation. Such distortion may well affect the spectral structure in the wide frequency range. This study suggests that the spectral structure of HRV should be interpreted carefully, taking the above distortion properties into account, even though the IPFM model appears to be established as a mechanism mediating between autonomic input and heart rate variability.

Further study on 1/f fluctuations observed in central single neurons during REM sleep

Recently, 1/f fluctuations have been discovered in the single-unit activity of mesencephalic reticular formation (MRF) neurons during REM sleep. In a previous paper, such behavior could satisfyingly be interpreted on the basis of the clustering Poisson process. The question of applicability of this model to other MRF neurons remained unanswered. The present paper reports on 1/f fluctuations in 12 MRF neurons all of which can satisfyingly be modeled by the clustering Poisson process.

Simulation study on dynamics transition in neuronal activity during sleep cycle by using asynchronous and symmetry neural network model

We have found that single neuronal activities in different regions in the brain commonly exhibit the distinct dynamics transition during sleep-waking cycle in cats. Especially, power spectral densities of single neuronal activities change their profiles from the white to the 1/f along with sleep cycle from slow wave sleep (SWS) to paradoxical sleep (PS). Each region has different neural network structure and physiological function. This suggests a globally working mechanism may be underlying the dynamics transition we concern. Pharmacological studies have shown that a change in a wide-spread serotonergic input to these regions possibly causes the neuronal dynamics transition during sleep cycle. In this paper, based on these experimental results, an asynchronous and symmetry neural network model including inhibitory input, which represents the role of the serotonergic system, is utilized to examine the reality of our idea that the inhibitory input level varying during sleep cycle induce that transition. Simulation results show that the globally applied inhibitory input can control the dynamics of single neuronal state evolution in the artificial neural network: 1/f-like power spectral density profiles result under weak inhibition, which possibly corresponds to PS, and white profiles under strong inhibition, which possibly corresponds to SWS. An asynchronous neural network is known to change its state according to its energy function. The geometrical structure of network energy function is thought to vary along with the change in inhibitory level, which is expected to cause the dynamics transition of neuronal state evolution in the network model. These simulation results support the possibility that the serotonergic system is essential for the dynamics transition of single neuronal activities during sleep cycle.

Structural properties of network attractor associated with neuronal dynamics transition

It was found that single neuronal activities in various regions in the brain commonly exhibit the distinct dynamics transition from the white to the a/f spectral profiles during the sleep cycle in cats. The dynamics transition was simulated by using a symmetrically connected neural network model including a globally applied inhibitory input. The structure of the network attractor was suggested to vary in association with the change in inhibitory level. To examine the robustness of the dynamics transition, the symmetry network structure is extended to the asymmetrically connected network model. This asymmetricity follows the rule which approximately reflects the characteristics of synaptic contacts between neurons. Computer simulations showed that the inhibitory input could change the neuronal dynamics from the white to the 1/f profiles under more realistic situations. The geometry of the network attractor realizing the dynamics transition is discussed.<<ETX>>

Robustness of 1/f fluctuations in P‐P intervals of cat’s electrocardiogram

Power spectral analyses have been performed on PP intervals of cat’s electrocardiogram recorded during about 100 hrs under 3 experimental conditions: one with hard exogenous disturbances and others with the minimal in either a small or a large range of movement of the animal. Irrespective of the marked differences in the experimental conditions, a very similar 1/f profile of power spectrum has been obtained in the frequency range, 10−5–10−1 Hz. The 1/f spectrum tended to extend to the frequency range below 10−5Hz. The 1/f fluctuations in heart beat periods seem to have endogenous origins.

Metastability of Network Attractor and Dream Sleep

We have found that single neuronal activities in various brain regions commonly exhibit the dynamics transition from the white to the 1/f spectral profiles during sleep cycle in cats. Simulations using the neural network model showed that the global inhibitory input could induce that transition. Especially, under the weak inhibition (the physiological situation of dream sleep), the metastability of the network attractor was found to be dominant. In this paper, the metastable behavior of the network is investigated for the symmetry and the asymmetry structures, which can be interpreted by the simple schemes based on the potential energy. The metastability could be a cue to understand the function of dream sleep.

Geometrie analysis of cardiovascular dynamics during sleep and wakefulness

We have investigated dynamics of cardiovascular variables during physiological states such as sleep and wakefulness. Their dynamics is modeled as a multidimensional autoregressive time series. Mutual geometric relations among state-dependent dynamics are obtained by means of the Kullback-Leibler divergence. The dynamics during sleep is markedly consistent compared to waking states within a subject. State-dependencies of cardiovascular dynamics and their mutual relations are suggested to uniquely characterize the autonomie regulation of cardiovascular system.

Simulation Study on Calcium-Activated Dynamics of Compartment Dendrite Model

Recent physiological experiments have proved that a neuronal dendritic tree in the central nervous system (CNS) possesses the complex spatio-temporal dynamics. The dynamical properties of dendrite are expected to play an important role for the information processing in CNS. From this point of view, we construct a compartment model of an active dendrite in a discrete form in order to investigate its functional significance. The dynamics of the single compartment model is controlled mainly by the following two kinetic variables. One is the membrane potential which involves the several types of currents: T-type Ca2+, L-type Ca2+, and Ca2+-activated K+. The other is the intracellular Ca2+ concentration that is increased by Ca2+ influx and reduced by the cellular homeostatic system. The single compartment model responds in an oscillatory manner when a maintained current stimulation is applied. Figure 1 shows the relationship between the response firing frequency and the current intensity. There is the threshold and the optimal intensity of stimulus for the oscillatory response.

Measurement of Neuromagnetic Fields Preceding Triggered and Self-Paced Finger Movements

In previous reports1–5 slow magnetic field shifts preceding voluntary finger or foot movements were detected from the brain. It was considered that the magnetic fields were generated from the motor area, premotor area, or supplementary motor area of the cortex. The signal sources were estimated for the magnetic field accompanying different kinds of movements. Those magnetic fields were called movement-related magnetic fields that include readiness fields(bereitschafts magnetic fields) and motor fields. We have carried out measurements of neuromagnetic fields for the purposes of comparing the neuronal activity preceding triggered movement by a visual signal and nontriggered (self-paced) movement.6

A Model-based Interpretation of Phantom Pain— Conservative Body Schema and Flexible Somatotopy—

Some patients with amputations are known to still experience presence of the amputated limb (phantom limb). Phantom limb is found to be accompanied by reorganizations of somatotopic representations at cortical and subcortical levels due to deafferentation, which also discloses presence of body schema, i.e., integrated image of body. In addition, phantom limb is often recognized with painful sensation, i.e., phantom pain, which is suggested to be caused by abnormally high activations in thalamocortical neurons in the deafferentated area. In this paper, simulations using a self-organizing neural network model were performed to clarify contribution of axonal sprouting and body schema to reorganization of cortical representation, and to explore a possible mechanism underlying abnormally high thalamocortical activation. The results showed that axonal sprouting could cause reorganization of cortical mapping with an aid of facilitatory inputs referring to the conserved body schema even after deafferentation. In addition, removing thalamic recurrent inhibitions was shown to induce high and sustained activations in thalamocortical neurons in the deafferentated area. Consequently, this study suggested that discordance between the recognized cortical representation and physiological process referring to the body schema could give rise to phantom pain. Phantom pain is a clue disclosing how body schema is constructed and maintained, which might be an important subject also for robotics dealing with an artificial body.