Mathematical neuroscience is required to understand the normal functions of the computational brain. As a corollary, we can translate our fundamental understanding of the nervous system to better understand and treat disorders of the brain.
There are a variety of brain diseases that are considered dynamical - where the symptoms are consequences of pathological parameters of the underlying neuronal elements and network. Several dynamical diseases have steadily improving mechanistic understanding, embodied into computational models that increasingly reflect the dynamics of the disease symptoms. These include Parkinson's disease and epilepsy. We suspect that other more complex brain diseases are revealing a dynamical component, such as depression and schizophrenia. Along with the advent of improving with computational models, the technical ability to perform open or closed loop deep brain stimulation is now becoming increasingly applied to treat these dynamical diseases. Thus as we better understand dynamical disease, the ability to dynamically probe and control them is now at hand.
Another area of medical application of mathematical theory is in the area of brain interfaces. Here, measurement arrays (electrodes or optical) can be used to extract dynamics from ensembles of neurons, and functions are created to decode such information. Such decodings enable us to understand the neural code, and to drive robotic devices or encode information to stimulate the brain. In addition to devices that can adapt to the brain's activity, it is now clear that the brain co-adapts to such devices - learning to use them to accomplish tasks. The cutting edge of interfacing with decoding from and encoding information for the brain is an important cutting edge of mathematical biosciences.
Lastly, genomic variability is an inherent aspect of the robustness of species --- it is the plausibility of life itself. Yet the dynamical expression of genomic variability may remain within, or branch across species boundaries --- speciation requires a phenotypic dynamical expression. In neuronal circuits, there is substantial variation in the levels of active channel proteins, and computationally, there are wide varieties of building equivalent dynamics from available genetic protein products. In epilepsy, there is increasing evidence that the combinatorics of multiple channel protein variations may contribute to producing similar expression of the dynamics of epilepsy. In depression, the autoreceptors and reuptake transporters on dopaminergic and serotonergic neurons change their expression levels in response to long-term changes in extracellular concentrations of neurotransmitters. Thus understanding the mechanisms by which SSRIs work necessarily involves understanding gene regulation, biochemistry, and electrophysiology, and how they influence each other dynamically in neurons. We will explore the intersection of evolution, genetic variability, and dynamical disease.