Cochlear implants are prosthetic devices used to provide hearing to people who would otherwise be profoundly deaf.
The deliberate addition of noise to the electrode signals could increase the amount of information transmitted, but
standard cochlear implants do not replicate the noise characteristic of normal hearing because if noise is added in an
uncontrolled manner with a limited number of electrodes then it will almost certainly lead to worse performance. Only if
partially independent stochastic activity can be achieved in each nerve fibre can mechanisms like suprathreshold
stochastic resonance be effective.
We are investigating the use of stochastic beamforming to achieve greater independence. The strategy involves
presenting each electrode with a linear combination of independent Gaussian noise sources. Because the cochlea is filled
with conductive salt solutions, the noise currents from the electrodes interact and the effective stimulus for each nerve
fibre will therefore be a different weighted sum of the noise sources. To some extent therefore, the effective stimulus for
a nerve fibre will be independent of the effective stimulus of neighbouring fibres.
For a particular patient, the electrode position and the amount of current spread are fixed. The objective is therefore to
find the linear combination of noise sources that leads to the greatest independence between nerve discharges. In this
theoretical study we show that it is possible to get one independent point of excitation (one null) for each electrode and
that stochastic beamforming can greatly decrease the correlation between the noise exciting different regions of the
We have investigated how optimal coding for neural systems changes with the time available for decoding.
Optimization was in terms of maximizing information transmission. We have estimated the parameters for
Poisson neurons that optimize Shannon transinformation with the assumption of rate coding. We observed a
hierarchy of phase transitions from binary coding, for small decoding times, toward discrete (M-ary) coding
with two, three and more quantization levels for larger decoding times. We postulate that the presence of
subpopulations with specific neural characteristics could be a signiture of an optimal population coding scheme
and we use the mammalian auditory system as an example.
We have previously advocated the deliberate addition of noise to cochlear implant signals to enhance the speech comprehension of cochlear implant users. The function of the additive noise is to mimic noise sources that are present in a healthy ear (originating, for example, from Brownian motion of the hair cells and the fluctuations induced by the opening and closing of ion channels) but are largely absent in a deafened ear where the hair cells have been damaged or destroyed. The normal ear, however, also contains multiplicative noise sources that result from the quantal nature of synaptic transmission between the inner hair-cells and the cochlear nerve. These noise synaptic noise sources are also largely absent in the deafened ear. Given that previous studies suggest that additive noise can enhance information coding by sensory systems, we have investigated whether multiplicative noise also enhances coding in a model of electrical stimulation of the cochlear nerve by a cochlear implant. The model was based on leaky integrate-and-fire dynamics and modelled refractory and accommodation effects by a threshold dependency derived from the sodium-inactivation dynamics of the Frankenhauser-Huxley equations for myelinated nerves. We show that multiplicative noise leads to a fundamental change in the coding mechanism and can lead to a marked increase in the transmitted information compared with additive noise or a control condition with no noise. These results suggest that multiplicative noise in the normal auditory system might have a functional role.
We have investigated information transmission in an array of threshold units with multiplicative noise that have a common input signal. We demonstrate a phenomenon similar to stochastic resonance with additive noise, and show that information transmission can be enhanced by a non-zero multiplicative noise level. Given that sensory neurons in the nervous system have multiplicative as well as additive noise sources, and they act approximately like threshold units, our results suggest that multiplicative noise might be an essential part of neural coding.
Cochlear implants are used to restore functional hearing to people with profound deafness. Success, as measured by speech intelligibility scores, varies greatly amongst patients; a few receive almost no benefit while some are able to use a telephone under favourable listening conditions. Using a novel nerve model and the principles of suprathreshold stochastic resonance, we demonstrate that the rate of information transfer through a cochlear implant system can be globally maximized by the addition of noise. If this additional information could be used by the brain then it would lead to greater speech intelligibility, which is important given that the intelligibility of all cochlear implant recipients is poorer than that of people with normal hearing, particularly in adverse listening conditions.
In this article we discuss the possible use of a novel form of stochastic resonance, termed suprathreshold stochastic resonance (SSR), to improve signal encoding/transmission in cochlear implants. A model, based on the leaky-integrate-and-fire (LIF) neuron, has been developed from physiological data and use to model information flow in a population of cochlear nerve fibers. It is demonstrated that information flow can, in principle, be enhanced by the SSR effect. Furthermore, SSR was found to enhance information transmission for signal parameters that are commonly encountered in cochlear implants. This, therefore, gives hope that SSR may be implemented in cochlear implants to improve speech comprehension.