A narrative review of the logistic and economic feasibility of cochlear implants in lower-income countries.

Globally, less than 1% of people who could benefit from a cochlear implant have one and the problem is particularly acute in lower-income countries. Here we give a narrative review of the economic and logistic feasibility of cochlear implant programmes in lower-income countries and discuss future developments that would enable better healthcare. We review the incidence and aetiology of hearing loss in low- and middle-income countries, screening for hearing loss, implantation criteria, issues concerning imaging and surgery, and the professional expertise required.

Suprathreshold Stochastic Resonance behind Cancer.

Noise in gene expression is pervasive and, in some cases, even fulfills a functional role. Cancer cell populations exploit noise to increase heterogeneity as a defense against therapies. What lies behind this picture is a phenomenon of stochastic resonance led by the collective, rather than by individual cells.

Enhanced processing in arrays of optimally tuned nonlinear biomimetic sensors: A coupling-mediated Ringelmann effect and its dynamical mitigation.

Inspired by recent results on self-tunability in the outer hair cells of the mammalian cochlea, we describe an array of magnetic sensors where each individual sensor can self-tune to an optimal operating regime. The self-tuning gives the array its "biomimetic" features. We show that the overall performance of the array can, as expected, be improved by increasing the number of sensors but, however, coupling between sensors reduces the overall performance even though the individual sensors in the system could see an improvement.

Enhancing the resolution of a sensor via negative correlation: a biologically inspired approach.

We demonstrate that a neuronal system, underpinned by "fire-then-reset" dynamics, can display an enhanced resolution R~T(ob)(-1) where T(ob) is the observation time of the measurement; this occurs when the interspike intervals are negatively correlated and T(ob)<Δ/ε, where ε is a parameter characterizing the level of correlation between interspike intervals and Δ is the average interspike interval. We also show that by introducing negative correlations into the time domain response of a nonlinear dynamical sensor it is possible to replicate this enhanced scaling of the resolution. Thus, we demonstrate the potential for designing a novel class of biomimetic sensors that afford improved signal resolution by functionally utilizing negative correlations.

Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.

Objective: An electroencephalogram-based (EEG-based) brain-computer-interface (BCI) provides a new communication channel between the human brain and a computer. Amongst the various available techniques, artificial neural networks (ANNs) are well established in BCI research and have numerous successful applications. However, one of the drawbacks of conventional ANNs is the lack of an explicit input optimization mechanism.

Phase locking below rate threshold in noisy model neurons.

The property of a neuron to phase-lock to an oscillatory stimulus before adapting its spike rate to the stimulus frequency plays an important role for the auditory system. We investigate under which conditions neurons exhibit this phase locking below rate threshold. To this end, we simulate neurons employing the widely used leaky integrate-and-fire (LIF) model.

Neural population coding is optimized by discrete tuning curves.

The sigmoidal tuning curve that maximizes the mutual information for a Poisson neuron, or population of Poisson neurons, is obtained. The optimal tuning curve is found to have a discrete structure that results in a quantization of the input signal. The number of quantization levels undergoes a hierarchy of phase transitions as the length of the coding window is varied.

Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations.

A general method for deriving maximally informative sigmoidal tuning curves for neural systems with small normalized variability is presented. The optimal tuning curve is a nonlinear function of the cumulative distribution function of the stimulus and depends on the mean-variance relationship of the neural system. The derivation is based on a known relationship between Shannon's mutual information and Fisher information, and the optimality of Jeffrey's prior.

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.