Enhancement of information transmission of sub-threshold signals applied to distal positions of dendritic trees in hippocampal CA1 neuron models with stochastic resonance

Stochastic resonance (SR) has been shown to enhance the signal-to-noise ratio and detection of low level signals in neurons. It is not yet clear how this effect of SR plays an important role in the information processing of neural networks. The objective of this article is to test the hypothesis that information transmission can be enhanced with SR when sub-threshold signals are applied to distal positions of the dendrites of hippocampal CA1 neuron models. In the computer simulation, random sub-threshold signals were presented repeatedly to a distal position of the main apical branch, while the homogeneous Poisson shot noise was applied as a background noise to the mid-point of a basal dendrite in the CA1 neuron model consisting of the soma with one sodium, one calcium, and five potassium channels. From spike firing times recorded at the soma, the mutual information and information rate of the spike trains were estimated. The simulation results obtained showed a typical resonance curve of SR, and that as the activity (intensity) of sub-threshold signals increased, the maximum value of the information rate tended to increased and eventually SR disappeared. It is concluded that SR can play a key role in enhancing the information transmission of sub-threshold stimuli applied to distal positions on the dendritic trees.     

The Sign Rule and Beyond: Boundary Effects,Flexibility, and Noise Correlations in Neural Population Codes

Over repeat presentations of the same stimulus, sensory neurons show variable responses. This “noise” is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population''s ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem – neural tuning curves, etc. – held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) — if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all.     

Engineered internal noise stochastic resonator in gene network: a model study

Based on a genetic bistable switch model coupled with a gene oscillator model, we have constructed a mesoscopic stochastic model for the coupled synthetic gene network, and studied how internal noise would influence the oscillation of such a system. We found that the state-to-state transitions can occur if the internal noise is taken into account, and the performance of resulting oscillation can reach a maximum in a certain internal noise level, which indicates the occurrence of internal noise stochastic resonance (SR) and makes the coupled gene network work as a stochastic resonator. The potential role of such an effect on gene expression systems is also discussed.     

Stochastic resonance modulates neural synchronization within and between cortical sources

Neural synchronization is a mechanism whereby functionally specific brain regions establish transient networks for perception, cognition, and action. Direct addition of weak noise (fast random fluctuations) to various neural systems enhances synchronization through the mechanism of stochastic resonance (SR). Moreover, SR also occurs in human perception, cognition, and action. Perception, cognition, and action are closely correlated with, and may depend upon, synchronized oscillations within specialized brain networks. We tested the hypothesis that SR-mediated neural synchronization occurs within and between functionally relevant brain areas and thus could be responsible for behavioral SR. We measured the 40-Hz transient response of the human auditory cortex to brief pure tones. This response arises when the ongoing, random-phase, 40-Hz activity of a group of tuned neurons in the auditory cortex becomes synchronized in response to the onset of an above-threshold sound at its "preferred" frequency. We presented a stream of near-threshold standard sounds in various levels of added broadband noise and measured subjects' 40-Hz response to the standards in a deviant-detection paradigm using high-density EEG. We used independent component analysis and dipole fitting to locate neural sources of the 40-Hz response in bilateral auditory cortex, left posterior cingulate cortex and left superior frontal gyrus. We found that added noise enhanced the 40-Hz response in all these areas. Moreover, added noise also increased the synchronization between these regions in alpha and gamma frequency bands both during and after the 40-Hz response. Our results demonstrate neural SR in several functionally specific brain regions, including areas not traditionally thought to contribute to the auditory 40-Hz transient response. In addition, we demonstrated SR in the synchronization between these brain regions. Thus, both intra- and inter-regional synchronization of neural activity are facilitated by the addition of moderate amounts of random noise. Because the noise levels in the brain fluctuate with arousal system activity, particularly across sleep-wake cycles, optimal neural noise levels, and thus SR, could be involved in optimizing the formation of task-relevant brain networks at several scales under normal conditions.     

Weak electric fields detectability in a noisy neural network

We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.     

Ubiquitous crossmodal Stochastic Resonance in humans: auditory noise facilitates tactile, visual and proprioceptive sensations

Background

Stochastic resonance is a nonlinear phenomenon whereby the addition of noise can improve the detection of weak stimuli. An optimal amount of added noise results in the maximum enhancement, whereas further increases in noise intensity only degrade detection or information content. The phenomenon does not occur in linear systems, where the addition of noise to either the system or the stimulus only degrades the signal quality. Stochastic Resonance (SR) has been extensively studied in different physical systems. It has been extended to human sensory systems where it can be classified as unimodal, central, behavioral and recently crossmodal. However what has not been explored is the extension of this crossmodal SR in humans. For instance, if under the same auditory noise conditions the crossmodal SR persists among different sensory systems.

Methodology/Principal Findings

Using physiological and psychophysical techniques we demonstrate that the same auditory noise can enhance the sensitivity of tactile, visual and propioceptive system responses to weak signals. Specifically, we show that the effective auditory noise significantly increased tactile sensations of the finger, decreased luminance and contrast visual thresholds and significantly changed EMG recordings of the leg muscles during posture maintenance.

Conclusions/Significance

We conclude that crossmodal SR is a ubiquitous phenomenon in humans that can be interpreted within an energy and frequency model of multisensory neurons spontaneous activity. Initially the energy and frequency content of the multisensory neurons'' activity (supplied by the weak signals) is not enough to be detected but when the auditory noise enters the brain, it generates a general activation among multisensory neurons of different regions, modifying their original activity. The result is an integrated activation that promotes sensitivity transitions and the signals are then perceived. A physiologically plausible model for crossmodal stochastic resonance is presented.     

Stochastic resonance emergence from a minimalistic behavioral rule

Stochastic resonance (SR) is a phenomenon occurring in nonlinear systems by which the ability to process information, for instance the detection of weak signals is statistically enhanced by a non-zero level of noise. SR effects have been observed in a great variety of systems, comprising electronic circuits, optical devices, chemical reactions and neurons. In this paper we report the SR phenomena occurring in the execution of an extremely simple behavioral rule inspired from bacteria chemotaxis. The phenomena are quantitatively analyzed by using Markov chain models and Monte Carlo simulations.     

Stochastic resonance induced by fluctuation in liquid membrane oscillator without input signals

We investigated numerically the dynamic behavior of the oil/water liquid membrane, which is a promising model for excitable bio-membrane. When we use noise to modulate the parameters in simulation, noise-induced coherent oscillation is observed. With the increment of the noise intensity, the coherence of noise-induced oscillation can go through a maximum, which indicating the occurrence of stochastic resonance (SR) without input signals. We compared the SR effects under the condition that noise is added to different control parameters. When noise was added to both of the parameters, a complicated SR-like phenomemon was observed. The interaction of coherent SRs induced by two independent noises is discussed. The possibly constructive role of noise in some sensory cells is discussed also.     

STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

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STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

The spike trains generated by a neuron model are studied by the methods of nonlinear time series analysis. The results show that the spike trains are chaotic. To investigate effect of noise on transmission of chaotic spike trains, this chaotic spike trains are used as a discrete subthreshold input signal to the integrate-and-fire neuronal model and the FitzHugh-Nagumo(FHN) neuronal model working in noisy environment. The mutual information between the input spike trains and the output spike trains is calculated, the result shows that the transformation of information encoded by the chaotic spike trains is optimized by some level of noise, and stochastic resonance(SR) measured by mutual information is a property available for neurons to transmit chaotic spike trains.     

Imaging performance of phase-contrast breast computed tomography with synchrotron radiation and a CdTe photon-counting detector

PurposeWithin the SYRMA-CT collaboration based at the ELETTRA synchrotron radiation (SR) facility the authors investigated the imaging performance of the phase-contrast computed tomography (CT) system dedicated to monochromatic in vivo 3D imaging of the female breast, for breast cancer diagnosis.MethodsTest objects were imaged at 38 keV using monochromatic SR and a high-resolution CdTe photon-counting detector. Signal and noise performance were evaluated using modulation transfer function (MTF) and noise power spectrum. The analysis was performed on the images obtained with the application of a phase retrieval algorithm as well as on those obtained without phase retrieval. The contrast to noise ratio (CNR) and the capability of detecting test microcalcification clusters and soft masses were investigated.ResultsFor a voxel size of (60 μm)³, images without phase retrieval showed higher spatial resolution (6.7 mm⁻¹ at 10% MTF) than corresponding images with phase retrieval (2.5 mm⁻¹). Phase retrieval produced a reduction of the noise level and an increase of the CNR by more than one order of magnitude, compared to raw phase-contrast images. Microcalcifications with a diameter down to 130 μm could be detected in both types of images.ConclusionsThe investigation on test objects indicates that breast CT with a monochromatic SR source is technically feasible in terms of spatial resolution, image noise and contrast, for in vivo 3D imaging with a dose comparable to that of two-view mammography. Images obtained with the phase retrieval algorithm showed the best performance in the trade-off between spatial resolution and image noise.     

Transformation of mouse mammary epithelial cells with the Ha-ras but not with the neu oncogene results in a gene dosage-dependent increase in transforming growth factor-alpha production

An enhanced expression of transforming growth factor-alpha (TGF alpha) was demonstrated in two clones of NOG-8 mouse mammary epithelial cells, NOG-8 SR1 and NOG-8 SR2, that have been transformed by a v-Ha-ras oncogene. The amount of TGF alpha production in NOG-8 SR1 and NOG-8 SR2 cells was dependent on the level of p21ras expression in these clones, which directly correlated with their cloning efficiency in soft agar. There was also a decrease in the number of epidermal growth factor (EGF) receptors on the NOG-8 SR1 and NOG-8 SR2 cells that is proportional to the amount of TGF alpha secreted. These effects were specific for ras because neu-transformed NOG-8 cells grew in soft agar at a comparable level to NOG-8 SR2 cells yet did not show any increase in TGF alpha production or change in EGF receptor expression.     

Resonances and noise in a stochastic Hindmarsh-Rose model of thalamic neurons

Thalamic neurons exhibit subthreshold resonance when stimulated with small sine wave signals of varying frequency and stochastic resonance when noise is added to these signals. We study a stochastic Hindmarsh-Rose model using Monte-Carlo simulations to investigate how noise, in conjunction with subthreshold resonance, leads to a preferred frequency in the firing pattern. The resulting stochastic resonance (SR) exhibits a preferred firing frequency that is approximately exponential in its dependence on the noise amplitude. In similar experiments, frequency dependent SR is found in the reliability of detection of alpha-function inputs under noise, which are more realistic inputs for neurons. A mathematical analysis of the equations reveals that the frequency preference arises from the dynamics of the slow variable. Noise can then transfer the resonance over the firing threshold because of the proximity of the fast subsystem to a Hopf bifurcation point. Our results may have implications for the behavior of thalamic neurons in a network, with noise switching the membrane potential between different resonance modes.     

Stochastic resonance in a sinusoidally forced LIF model with noisy threshold

In this report, the LIF neural model driven by underthreshold sinusoidal signals but with a gaussian-distributed noise on the threshold, is approximated by suitably defining an instantaneous firing (or escape) rate, which depends only on the momentary value of the voltage variable. This allows us to obtain, by analytically solving the relevant equations, the main statistical functions describing the "firing activity"; namely, the probability density function of firing phases and that of interspike intervals. From these functions two quantities can be derived, whose dependence on the noise intensity allows the Stochastic Resonance (SR) to be demonstrated. Besides the "regular" SR, the analysed system was found to produce, either for low frequencies and large amplitudes of modulation or for high modulation frequencies, resonance curves displaying two peaks. This bimodal feature of the resonance curves is accounted for on the basis of phase locked firing patterns.     

Slope-Based Stochastic Resonance: How Noise Enables Phasic Neurons to Encode Slow Signals

Fundamental properties of phasic firing neurons are usually characterized in a noise-free condition. In the absence of noise, phasic neurons exhibit Class 3 excitability, which is a lack of repetitive firing to steady current injections. For time-varying inputs, phasic neurons are band-pass filters or slope detectors, because they do not respond to inputs containing exclusively low frequencies or shallow slopes. However, we show that in noisy conditions, response properties of phasic neuron models are distinctly altered. Noise enables a phasic model to encode low-frequency inputs that are outside of the response range of the associated deterministic model. Interestingly, this seemingly stochastic-resonance (SR) like effect differs significantly from the classical SR behavior of spiking systems in both the signal-to-noise ratio and the temporal response pattern. Instead of being most sensitive to the peak of a subthreshold signal, as is typical in a classical SR system, phasic models are most sensitive to the signal''s rising and falling phases where the slopes are steep. This finding is consistent with the fact that there is not an absolute input threshold in terms of amplitude; rather, a response threshold is more properly defined as a stimulus slope/frequency. We call the encoding of low-frequency signals with noise by phasic models a slope-based SR, because noise can lower or diminish the slope threshold for ramp stimuli. We demonstrate here similar behaviors in three mechanistic models with Class 3 excitability in the presence of slow-varying noise and we suggest that the slope-based SR is a fundamental behavior associated with general phasic properties rather than with a particular biological mechanism.     

Solubilization and separation of Ca2+-ATPase from the Ca2+-ryanodine receptor complex

Heavy sarcoplasmic reticulum (SR) preparations of rabbit skeletal muscle, which are enriched in Ca2+-release vesicles from the terminal cisternae (TC) and [3H]ryanodine receptor density, exhibit 60% of the Ca2+-ATPase activity, 58% of the EP level, and 30% of the steady state Ca2+ loading compared to membrane vesicles from the longitudinal SR. The Ca2+-ATPase of TC SR is solubilized and separated from the Ca2+-ryanodine receptor complex in the insoluble fraction on treatment with the detergent C12E9. However, a 50% decrease in receptor density is observed upon removal of the Ca2+-ATPase, suggesting a significant contribution of this protein to maintaining optimal receptor complex density.     

Complex effects of ryanodine on the sarcoplasmic reticulum Ca2+ levels in smooth muscle cells

We have studied the effects of ryanodine and inhibition of the sarco/endoplasmic reticulum Ca(2+) ATPase (SERCA) with thapsigargin, on both [Ca(2+)](i) and the sarcoplasmic reticulum (SR) Ca(2+) level during caffeine-induced Ca(2+) release in single smooth muscle cells. Incubation with 10 microM ryanodine did not inhibit the first caffeine-induced [Ca(2+)](i) response, although it abolished the [Ca(2+)](i) response to a second application of caffeine. To assess whether ryanodine was inducing a permanent depletion of the internal Ca(2+) stores, we measured the SR Ca(2+) level with Mag-Fura-2. The magnitude of the caffeine-induced reduction in the SR Ca(2+) level was not augmented by incubating cells with 1 microM ryanodine. Moreover, on removal of caffeine, the SR Ca(2+) levels partially recovered in 61% of the cells due to the activity of thapsigargin-sensitive SERCA pumps. Unexpectedly, 10 microM ryanodine instead of inducing complete depletion of SR Ca(2+) stores markedly reduced the caffeine-induced SR Ca(2+) response. It was necessary to previously inhibit SERCA pumps with thapsigargin for ryanodine to be able to induce caffeine-triggered permanent depletion of SR Ca(2+) stores. These data suggest that the effect of ryanodine on smooth muscle SR Ca(2+) stores was markedly affected by the activity of SERCA pumps. Our data highlight the importance of directly measuring SR Ca(2+) levels to determine the effect of ryanodine on the internal Ca(2+) stores.     

Lipid peroxidation and the functioning of the Ca pump of the sarcoplasmic reticulum of skeletal muscle in hypercholesterolemia

During prolonged hypercholesterolemia (HCh), there was a reduction in the efficacy of Ca-pump function of the sarcoplasmic reticulum (SR) of rabbit skeletal muscles, an increase in cholesterol content, and intensification of lipid peroxidation in SR membranes. Supplementation of the cholesterol-rich diet with alpha-tocopherol effectively prevented dysfunction of the SR Ca-pump and reduced the enhanced LPO level in SR membranes without having any effect on cholesterol content in blood serum and SR membranes. The increase in the LPO level seems likely to be primarily responsible for abnormalities in the SR Ca-pump during HCh.     

Increased inhibition of SERCA2 by phospholamban in the type I diabetic heart

The sarcoplasmic reticulum (SR) plays a critical role in mediating cardiac contractility and its function is abnormal in the diabetic heart. However, the mechanisms underlying SR dysfunction in the diabetic heart are not clear. Because protein phosphorylation regulates SR function, this study examined the phosphorylation state of phospholamban, a key SR protein that regulates SR calcium (Ca2+) uptake in the heart. Diabetes was induced in male Sprague-Dawley rats by an injection of streptozotocin (STZ; 65 mg kg(-1) i.v.), and the animals were humanely killed after 6 weeks and cardiac SR function was examined. Depressed cardiac performance was associated with reduced SR Ca2+-uptake activity in diabetic animals. The reduction in SR Ca2+-uptake was consistent with a significant decrease in the level of SR Ca2+-pump ATPase (SERCA2a) protein. The level of phospholamban (PLB) protein was also decreased, however, the ratio of PLB to SERCA2a was increased in the diabetic heart. Depressed SR Ca2+-uptake was also due to a reduction in the phosphorylation of PLB by the Ca2+-calmodulin-dependent protein kinase (CaMK) and cAMP-dependent protein kinase (PKA). Although the activities of the SR-associated Ca2+-calmodulin-dependent protein kinase (CaMK), cAMP-dependent protein kinase (PKA) were increased in the diabetic heart, depressed phosphorylation of PLB could partly be attributed to an increase in the SR-associated protein phosphatase activities. These results suggest that there is increased inhibition of SERCA2a by PLB and this appears to be a major defect underlying SR dysfunction in the diabetic heart.