We study minimal multistable systems of coupled model neurons with
combined excitatory and inhibitory connections. With slow potassium
currents, multistability of several firing regimes with
distinctively different firing rates is observed. In the presence of
noise, there is noise-driven switching between these states of which
transient dynamics have 1/f-type power spectra. The selection between higher- and lower-frequency oscillations depends on external inputs, which results in coherence between the periodic input and the system's firing rate. Without slow potassium currents, there are multistable solutions in which two inhibitory neurons fire
synchronously or anti-synchronously. Addition of a small amount of
noise results in increased synchronizability of the two neurons
depending on the level of external inputs. These results suggest
adaptable dynamics of multistable neural attractors to external
inputs enhanced by additional noise.
We demonstrate experimentally that the human brain can make use of externally added noise to modulate attention switching between spatial locations. To do this we implemented a psychophysical task. Subjects were asked to respond to a weak gray-level target presented inside a marker box either in the left or right visual field while they fixated a central cross. Signal detection performance was improved by presenting a low level of randomly flickering gray-level noise between and outside the two possible target locations, and worsened by higher levels of noise. Our results suggest that noise can optimize switching behavior between multistable attentional states of the human brain via the mechanism of stochastic resonance.
Using the method of local Continuous Detrended Fluctuation Analysis CDFA) we analyze the correlations of ventricular interbeat intervals of patients with Atrial Fibrillation (AF). CDFA yields a local Hoelder exponent h for a neighborhood around each point in the time series by determining the scaling of fluctuations with window size after detrending. We compare the histograms of Hoelder exponents for original data with those of randomly shuffled data and find some correlations not only in long-range windows but also at short time scales where interbeat intervals during AF have been believed to be random in nature. Furthermore, we find unique temporal correlation structures to occur only in the heart rate of patients who were in the survivor group when a follow up was conducted at least one year after data acquisition. We conclude that ventricular interbeat intervals during AF contain richer information than previously considered and the study of the local correlations may be useful in predicting mortality of the patients.
We demonstrate experimentally that enhanced detection of weak visual signals by addition of visual noise is accompanied by an increase in phase synchronization of EEG signals across widely-separated areas of the human brain. In our sensorimotor integration task, observers responded to a weak rectangular gray-level signal presented to their right eyes by pressing and releasing a button whenever they detected an increment followed by a decrement in brightness. Signal detection performance was optimized by presenting randomly-changing-gray-level noise separately to observers' left eyes using a mirror stereoscope. We measured brain electrical activity at the scalp by electroencephalograph (EEG), calculated the instantaneous phase for each EEG signal, and evaluated the degree of large-scale phase synchronization between pairs of EEG signals. Dynamic synchronization-desynchronization patterns were observed and we found evidence of noise-enhanced large-scale synchronization associated with detection of the brightness changes under conditions of noise-enhanced performance. Our results suggest that behavioral stochastic resonance might arise from noise-enhanced synchronization of neural activities across widespread brain regions.
We study a system of globally coupled FitzHugh-Nagumo equations as a stochastic resonator. Each unit is either excitatory or inhibitory. If the numbers of units of both types are nearly equal (balanced coupling), we observe the presence of multistable oscillatory states with different excitation or firing rates. In the presence of noise, random transitions between high- and low-frequency oscillatory states are observed and the resultant firing pattern is long-range correlated. Compared to other coupling types, the system demonstrates considerably improved rate-coding ability for both subthreshold and suprathreshold signals, even with a tiny level of noise.
We present the first systematic evidence for the origins and breakdown
of 1/f scaling in human heart rate. We confirm a previously posed conjecture that 1/f scaling in heart rate is caused by the intricate balance between antagonistic activity of sympathetic (SNS) and parasympathetic (PNS) nervous systems. We demonstrate that modifying the relative importance of either of the two branches leads to a substantial decrease of 1/f scaling. In particular, the relative PNS suppression both by congestive heart failure (CHF) and by the parasympathetic blocker atropine results in a substantial increase in the Hurst exponent H and a shift of the multifractal spectrum f(α) from 1/f towards random walk scaling 1/f2. Surprisingly, we observe a similar breakdown in the case of relative and neurogenic SNS suppression by primary autonomic failure (PAF). Further, we observe an intriguing interaction between multifractality of heart rate and absolute variability. While it is generally believed that lower absolute variability results in monofractal behaviour, as has been demonstrated both for CHF and the parasympathetic blockade, in PAF
patients we observe conservation of multifractal properties at
substantially reduced absolute variability to levels closer to
CHF. This novel and intriguing result leads us to the conjecture that
the multifractality of the heart rate can be traced back to the
intrinsic dynamics of the parasympathetic nervous system.
Human cardiovascular and/or cardio-respiratory systems are shown
to exhibit both multifractal and synchronous dynamics, and we
recently developed a nonlinear, physiologically plausible model
for the synchronization between heartbeat and respiration
(Kotani, et al. Phys. Rev. E 65: 051923, 2002). By using the same model, we now show the multifractality in the heart rate dynamics. We find that beat-to-beat monofractal noise (fractional Brownian motion) added to the brain stem cardiovascular areas results in significantly broader singularity spectra for heart rate
through interactions between sympathetic and parasympathetic
nervous systems. We conclude that the model proposed here would
be useful in studying the complex cardiovascular and/or cardio-
respiratory dynamics in humans.
We report the results of two psychophysics experiments showing that the human brain can make use of externally added noise for behavioral responses. Subjects were instructed to respond to changing gray level signals presented to their right eye. The behavioral responses were optimized by presenting randomly changing gray level noise to their left eye. The results indicate that the behavioral stochastic resonance occurs at the cortical level where information from both eyes merges together.
We hypothesized that 1/f noise is more beneficial than the conventional white noise in optimizing the brain's response to a weak input signal, and showed that externally added 1/f noise outperforms white noise in sensitizing human baroreflex centers in the brain. We examined the compensatory heart rate response to weak periodic signal introduced at the venous blood pressure receptor, while adding either 1/f or white noise with the same variance to the brain stem by electrically stimulating the bilateral vestibular afferents cutaneously. This stochastic galvanic vestibular stimulation, activating the vestibulo-sympathetic pathway in the brain stem, optimized covariance between weak input signals and the heart rate responses both with 1/f and white noise. Further, the optimal noise level with 1/f noise was significantly lower than that with white noise, suggesting the functional benefit of 1/f noise for the neuronal information transfer in the brain.
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