Computational implications of microcircuit specializations in forebrain circuits for motivated action selection

Recent decades have seen dramatic progress in the brain sciences. Much attention has been attracted by non-invasive techniques, such as fMRI, which enable imaging of brain activities that support human cultural behavior, such as language. Less attention is paid to the cumulative progress in neural path-tracing and microcircuit specification in primates. The flood of such information poses a huge challenge for computational neuroscientists striving to bridge from microcircuits to flexible cognition, and to build models that can accurately predict individual differences in efficacy of neurological therapies. Recent models have made progress toward specifying how cortical circuits that enable planning and voluntary actions interact with adaptive sub-cortical microcircuits in the basal ganglia. The basal ganglia are key to understanding voluntary behavior, because they are strongly implicated in reinforcement learning and in all behavior over which the frontal lobes exert flexible control. This key role of the basal ganglia shows that ancient vertebrate designs have proven adaptable enough to support recent primate innovations, including speech and language generation. This paper summarizes recent models that have incorporated realistic representations of microcircuit features, and traced their computational implications. These efforts extend an emerging theoretical synthesis based on an interlocking set of computational hypotheses regarding frontal cortex interactions with basal ganglia. These hypotheses specify how microcircuits utilize specialized electrical and chemical connections to solve learning and procedural control problems inherent to a massively parallel system.