Neuroengineering models of brain disease

The techniques of computational simulation have begun to be applied to modeling neurological disease and mental illness. Such neuroengineering models provide a conceptual bridge between molecular/cellular pathology and cognitive performance. We consider models of Alzheimer's disease, Parkinson's disease, and schizophrenia. Each of these diseases involves a disorder of neuromodulation coupled with underlying neuronal pathology. Parallels arising between these models suggests that a common set of computational mechanisms may account for functional loss across a spectrum of brain diseases. In particular, we focus on attractor-based network dynamics and how they arise from neural architectures, on mechanisms for linking sequences of attractor states and their role in cognition, and on the role of neuromodulation in controlling these processes. These studies suggest new approaches to understanding the forebrain circuits underlying cognition, and point toward a new tool for dissecting the pathophysiology of brain disease.