Grant: Discovery Grants Program - Individual

It is through our senses that we perceive what goes on in our environment. The first neurons in contact with the external world behave as generalists: they are exposed to physical and chemical stimuli that contain all the necessary information for our survival, and further convert that information into an internal "brain representation". Their job is further to pass on this information to more specialist neurons that focus on some features more than others, such as whether a stimulus is there or not, whether it is increasing, or poses a danger. This proposal aims to reveal the rules governing this conversion from external stimuli as brain inputs to the digital output of the first neurons, and then from these to the output of the next neurons. The result will be a deeper understanding on the internal language of the brain, i.e. of the "neural code", which is my LONG-TERM GOAL. Apart from pursuing this key scientific quest for the brain code, this work is important in practice to understand our relationship to the environment, and provide a more informed guide to enhancing brain circuitry or repairing it when it is faulty. For the next five years, I will train 5 undergraduates, 5 MSc and 6 PhD. Objective 1 seeks rules by which many animals achieve ultrasensitivity, detecting extraordinarily small stimuli that are swamped by environmental fluctuations of all kind. Objective 2 focuses on how the brain processes temperature information, by exposing the rules for representing increases and decreases in temperature. Our ability to deal with temperature is compromised when the insulating myelin sheath around axons thins away with the years, and in certain diseases, and the related hope is to better understand how temperature affects neurons in general and can compensate for such loss. The final Objective 3 deals again with coding and noise, but pushed up to the context of whole brain rhythms involved in cognition. By looking at neural coding at different scales, from single generalist neurons to large brain circuits, it is likely that general rules for how the brain does its work, and the underlying language it uses, can be more easily revealed.  The work is theoretical and computational in nature. We formulate predictive mathematical models that are simple enough to explain neural phenomena with a minimal number of biological ingredients. The work will make careful use of existing biological data, and benefit from collaborations with experimental neurobiologists. The modelling also serves the purpose of making predictions about function - via theoretical analyses or in silico experiments - which can be tested in new experiments. This research will deepen our knowledge of brain circuit function. It may also lead to novel sensing technologies (e.g. neural prosthetics) based on newly found principles, including technologies to sense heat and electric fields, or to repair pathological brain rhythms.