Synaptic plasticity regulates neuronal responses to patterns of inputs impinging on dendritic arbors from multiple presynaptic sources. This 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 toward ever greater complexity and biological realism. We hypothesize that similar dynamic set points and modulations exist in the brain's resting state activity, and that different modifications to information-based exchange may shift the risk profile of different component tissues, resulting in different neurodegenerative diseases. By implementing a system for maximum information-based exchange of action potentials between modeled neocortical areas, we observe changes to these measures in simulation. Our closed-loop model is grounded in neuroanatomical observations, proposing a novel “Grand Loop” through neocortex, and invokes different forms of plasticity at specific tissue interfaces and their principle cell synapses to achieve these transitions. 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 neuromodulation and other perturbations. Here we describe an “information-based exchange” model of brain function that ascribes to neocortex, basal ganglia, and thalamus distinct network functions.

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