  Here we describe an "information based exchange" model of brain function that
ascribes to neocortex, basal ganglia, and thalamus distinct network functions.
The model allows us to analyze whole brain system set point measures, such as
the rate and heterogeneity of transitions in striatum and neocortex, in the
context of disease perturbations. Our closed-loop model invokes different forms
of plasticity at specific tissue interfaces and their principle cell synapses
to achieve these transitions. By modulating information based exchange of
action potentials between modeled neocortical areas, we observe changes to
these measures in simulation. We hypothesize that similar dynamic set points
and modulations exist in the brain's resting state activity, and that germ line
modifications of information based exchange may increase the risk of diseases
such as Huntington's, Parkinson's, and Alzheimer's. Disturbances in synaptic
plasticity at distinct tissue interfaces in the model may be used to estimate
risks of system dysfunction and neuronal cell death from quantitative analyses
of the global dynamics that maintain system set points. The model is targeted
for further development using IBM's Neural Tissue Simulator, which allows
scalable elaboration of networks, tissues, and their neural and synaptic
components towards ever greater complexity and biological realism. Elaboration
of these simulations within each modeled neural tissue allows in silico study
of therapeutic interventions in living brain tissue.
