Neurocomputational models of brain disorders

Recent decades have witnessed dramatic accumulation of knowledge about the genetic, molecular, pharmacological, neurophysiological, anatomical, imaging and psychological characteristics of brain disorders. Despite these advances, however, experimental brain science has offered very little insight into the theoretical framework for integrating neurobiological and psychological data. Surgical treatment of neurological disorders like Parkinson’s disease, dystonia and epilepsy were until recently mainly based on applying lesions at specific parts of the brain. While these procedures nowadays have been replaced by more reversible neurostimulation methods, most therapies for brain disorders are still based on trial-and-error and effective mechanisms remain unknown. The goal of the special issue is to provide insights into neuronal network processes and interactions underlying normal and abnormal behavior based on computational models. These models describe network behavior at a microscopic (cellular) or macroscopic (system) level. The usefulness of the models in understanding neural organization and behavior is emphasized. The neuroanatomical and neurophysiological principles that are included in the models are clearly stated including the simplifications that are adopted. Experimental data is presented that form the basis for the acceptance of the model and its reductions both in describing normal and abnormal behavior.