Balancing resource and energy usage for optimal performance in a neural system

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The plasticity of the brain is one of the great scientific challenges and is of enormous interest in the general community because of the implications it has for the brain being able to repair itself, or be lent "a helping hand" by appropriate neural therapies and prostheses (see for example the popular book, "The Brain that Changes Itself" by Norman Doidge, Penguin 2007). Our work will provide a focussed, but hopefully significant new insight into the processes by which the brain can adjust itself to changing circumstances.We will use computer simulations of mathematical models built from experimental data to explore the operation of an early stage of the mammalian auditory system. We will study how this brain region dynamically configures itself to meet the operational demands of incoming 'information' about sounds in the environment, encoded by the activity of neurons in the cochlear nucleus. The brain is a complex and dynamic information processing system that is built from a large, but finite set of noisy components (cells and associated extracellular and intracellular signalling systems) and must operate in an energy efficient way. We will test the hypothesis that specific plasticity mechanisms adjust neurons in this brain region differently depending on whether they are processing high or low frequency sounds. Further, we postulate that plasticity is also trying to minimise the energy used by the neurons, and that this might be in conflict with the optimum processing of incoming auditory information.The increased understanding of the brain's intrinsic plasticity resulting from this project will ultimately have implications for the development of neural therapies. Treatments for neural dysfunction inevitably invoke intrinsic neural plasticity mechanisms that might enhance or even hinder the treatment. Of specific interest here is the development of cochlear implants to treat impaired hearing that cannot be compensated for by conventional hearing aids. These implants generate electrical signals in response to sounds and stimulate either the auditory nerve (most commonly) or the cochlear nucleus. Remarkable results have already been achieved with implants whose signals have only a fraction of the resolution and dynamic range of an intact cochlear (Wilson & Dorman (2008) Cochlear implants: Current designs and future possibilities, Journal of Rehabilitation Research & Development 45:695-730). This is entirely due to the brain's ability to adapt. Despite this success, improvements in cochlear implants will come through an improved understanding of the intrinsic plasticity mechanisms that are being invoked by the implant's stimulation. To quote from Wilson & Dorman (2008): "Cochlear implants work as a system, in which all parts are important, including the microphone, the processing strategy, the transcutaneous link, the receiver/stimulator, the implanted electrodes, the functional anatomy of the implanted cochlea, and the user's brain. Among these, the brain has received the least attention in implant designs to date." Our work will provide data on the mechanisms and theories of the implications of intrinsic plasticity in the brainstem auditory system. A further aspect of this project that needs increased public awareness is our use of a "systems" approach to studying a neural system. This has two aspects: (1) taking a holistic view of neural function that includes aspects such as activity-dependent regulation, noise and energy consumption, and (2) a tightly integrated programme of experiments and computational modelling. People are familiar with the use of computers in weather forecasting and climate change predictions, but there is less awareness of their use in computational biology and neuroscience. Appropriate dissemination of our work can give a snapshot of how computers and experiments together can provide insight into the detailed workings of the nervous system.