Stochastic resonance in the mechanoelectrical transduction of hair cells

No evidence for stochastic resonance effects on standing balance when applying noisy galvanic vestibular stimulation in young healthy adults

Noisy galvanic vestibular stimulation (nGVS) at imperceptible levels has been shown to reduce body sway. This reduction was commonly attributed to the mechanism of stochastic resonance (SR). However, it has never been explicitly tested whether nGVS-induced effects on body sway consistently follow a SR-like bell-shaped performance curve with maximal reductions in a particular range of noise intensities. To test this, body sway in 21 young healthy participants was measured during varying nGVS amplitudes while standing with eyes closed in 3 conditions (quiet stance, sway referencing, sinusoidal platform tilts). Presence of SR-like response dynamics in each trial was assessed (1) by a goodness-of-fit analysis using an established SR-curve model and (2) by ratings from 3 human experts. In accordance to theory, we found reductions of body sway at one nGVS amplitude in most trials (75–95%). However, only few trials exhibited SR-like bell-shaped performance curves with increasing noise amplitudes (10–33%). Instead, body sway measures rather fluctuated randomly across nGVS amplitudes. This implies that, at least in young healthy adults, nGVS effects on body sway are incompatible with SR. Thus, previously reported reductions of body sway at particular nGVS intensities more likely result from inherent variations of the performance metric or by other yet unknown mechanisms.

Control of in vivo ictogenesis via endogenous synaptic pathways

The random nature of seizures poses difficult challenges for epilepsy research. There is great need for a reliable method to control the pathway to seizure onset, which would allow investigation of the mechanisms of ictogenesis and optimization of treatments. Our hypothesis is that increased random afferent synaptic activity (i.e. synaptic noise) within the epileptic focus is one endogenous method of ictogenesis. Building upon previous theoretical and in vitro work showing that synaptic noise can induce seizures, we developed a novel in vivo model of ictogenesis. By increasing the excitability of afferent connections to the hippocampus, we control the risk of temporal lobe seizures during a specific time period. The afferent synaptic activity in the hippocampus was modulated by focal microinjections of potassium chloride into the nucleus reuniens, during which the risk of seizure occurrence increased substantially. The induced seizures were qualitatively and quantitatively indistinguishable from spontaneous ones. This model thus allows direct control of the temporal lobe seizure threshold via endogenous pathways, providing a novel tool in which to investigate the mechanisms and biomarkers of ictogenesis, test for seizure threshold, and rapidly tune antiseizure treatments.

Harvesting wind energy to detect weak signals using mechanical stochastic resonance

Wind is free and ubiquitous and can be harnessed in multiple ways. We demonstrate mechanical stochastic resonance in a tabletop experiment in which wind energy is harvested to amplify weak periodic signals detected via the movement of an inverted pendulum. Unlike earlier mechanical stochastic resonance experiments, where noise was added via electrically driven vibrations, our broad-spectrum noise source is a single flapping flag. The regime of the experiment is readily accessible, with wind speeds ∼20 m/s and signal frequencies ∼1 Hz. We readily obtain signal-to-noise ratios on the order of 10 dB.

NOISE-ENHANCED TRANSMISSION OF TIME-MODULATED NEUROTRANSMITTER RANDOM POINT TRAINS IN A NOISY NEURON

We numerically investigate the transmission of time-modulated random point trains in a conductance-based neuron model by including shot noise described as additive noise trains. The results show that additive noise trains can induce neuron responses exhibiting correlation with the temporally modulated random point trains. In addition, the additive noise power density can be increased up to an optimal value where the output signal-noise ratio (SNR) reaches a maximum value. This property of noise-enhanced transmission of random point trains can be related to the stochastic resonance (SR) phenomenon. More interestingly, we find that the SNR gain can exceed unity and can also be optimized by tuning the average rate of the input random point trains. The present study illustrates the potential to utilize the additive noise and temporally modulated random point trains for optimizing the response of the neuron to inputs, as well as a guidance in the design of information processing devices to random neuron spiking.

Stochastic resonance in the synaptic transmission between hair cells and vestibular primary afferents in development

The stochastic resonance (SR) is a phenomenon of nonlinear systems in which the addition of an intermediate level of noise improves the response of such system. Although SR has been studied in isolated hair cells and in the bullfrog sacculus, the occurrence of this phenomenon in the vestibular system in development is unknown. The purpose of the present study was to explore for the existence of SR via natural mechanical-stimulation in the hair cell-vestibular primary afferent transmission. In vitro experiments were performed on the posterior semicircular canal of the chicken inner ear during development. Our experiments showed that the signal-to-noise ratio of the afferent multiunit activity from E15 to P5 stages of development exhibited the SR phenomenon, which was characterized by an inverted U-like response as a function of the input noise level. The inverted U-like graphs of SR acquired their higher amplitude after the post-hatching stage of development. Blockage of the synaptic transmission with selective antagonists of the NMDA and AMPA/Kainate receptors abolished the SR of the afferent multiunit activity. Furthermore, computer simulations on a model of the hair cell - primary afferent synapse qualitatively reproduced this SR behavior and provided a possible explanation of how and where the SR could occur. These results demonstrate that a particular level of mechanical noise on the semicircular canals can improve the performance of the vestibular system in their peripheral sensory processing even during embryonic stages of development.

Modelling a historic oil-tank fire allows an estimation of the sensitivity of the infrared receptors in pyrophilous Melanophila beetles

Pyrophilous jewel beetles of the genus Melanophila approach forest fires and there is considerable evidence that these beetles can detect fires from great distances of more than 60 km. Because Melanophila beetles are equipped with infrared receptors and are also attracted by hot surfaces it can be concluded that these infrared receptors are used for fire detection.

The sensitivity of the IR receptors is still unknown. The lowest threshold published so far is 0.6 W/m² which, however, cannot explain the detection of forest fires by IR radiation from distances larger than approximately 10 km. To investigate the possible sensitivity of the IR receptors we assumed that beetles use IR radiation for remote fire detection and we made use of a historic report about a big oil-tank fire in Coalinga, California, in 1924. IR emission of an oil-tank fire can be calculated by “pool fire” simulations which now are used for fire safety and risk analysis. Assuming that beetles were lured to the fire from the nearest forests 25 and 130 km away, our results show that detection from a distance of 25 km requires a threshold of the IR receptors of at least 3×10⁻² W/m². According to our investigations most beetles became aware of the fire from a distance of 130 km. In this case the threshold has to be 1.3×10⁻⁴ W/m². Because such low IR intensities are buried in thermal noise we suggest that the infrared sensory system of Melanophila beetles utilizes stochastic resonance for the detection of weak IR radiation. Our simulations also suggest that the biological IR receptors might be even more sensitive than uncooled technical IR sensors. Thus a closer look into the mode of operation of the Melanophila IR receptors seems promising for the development of novel IR sensors.

Network recruitment to coherent oscillations in a hippocampal computer model

Coherent neural oscillations represent transient synchronization of local neuronal populations in both normal and pathological brain activity. These oscillations occur at or above gamma frequencies (>30 Hz) and often are propagated to neighboring tissue under circumstances that are both normal and abnormal, such as gamma binding or seizures. The mechanisms that generate and propagate these oscillations are poorly understood. In the present study we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of stochastic resonance and coherence resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. "Epileptic" high-frequency oscillations (HFOs), produced by pathologies such as increased synaptic activity and recurrent connections, were superior at recruiting neighboring tissue. "Normal" HFOs, associated with fast firing of inhibitory cells and sparse pyramidal cell firing, tended to suppress surrounding cells and showed very limited ability to recruit. These findings point to synaptic noise and physiological coupling as important targets for understanding the generation and propagation of both normal and pathological HFOs, suggesting potential new diagnostic and therapeutic approaches to human disorders such as epilepsy.

Stochastic resonance and self-tuning: a simple threshold system

To put it in the framework of information processing, stochastic resonance (SR) is a phenomenon in which the transfer of information from input to output signals can be significantly increased by noise with appropriate (nonzero) intensity. The minimum requirements for SR are generally input signals, nonlinearity, and noise, in addition to a quantifier to measure the efficiency of information transfer. We study a simple threshold system and propose an adaptation mechanism for system parameters, such as a threshold and noise intensity (i.e., temperature), which are held fixed in a normal SR setting. Our emphasis is put on parameter dynamics induced by this adaptation, which we call self-tuning (ST), and on how this dynamics modifies or changes the SR picture with respect to information processing efficiency. As a measure for performance of the threshold system we take mutual information and the signal-to-noise ratio. These quantities are calculated by an analytical method and by a simulational one. ST of temperature results in oscillatory time variation in temperature with its average located around the temperature at which performance is maximized in the SR setting. ST of a threshold turns out to improve performance in the weak noise region.

Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance

Suprathreshold stochastic resonance (SSR) is a form of noise-enhanced signal transmission that occurs in a parallel array of independently noisy identical threshold nonlinearities, including model neurons. Unlike most forms of stochastic resonance, the output response to suprathreshold random input signals of arbitrary magnitude is improved by the presence of even small amounts of noise. In this paper, the information transmission performance of SSR in the limit of a large array size is considered. Using a relationship between Shannon's mutual information and Fisher information, a sufficient condition for optimality, i.e., channel capacity, is derived. It is shown that capacity is achieved when the signal distribution is Jeffrey's prior, as formed from the noise distribution, or when the noise distribution depends on the signal distribution via a cosine relationship. These results provide theoretical verification and justification for previous work in both computational neuroscience and electronics.