Computational perspectives on forebrain microcircuits implicated in reinforcement learning, action selection, and cognitive control.
Abundant new information about signaling pathways in forebrain microcircuits presents many challenges, and opportunities for discovery, to computational neuroscientists who strive to bridge from microcircuits to flexible cognition and action. Accurate treatment of microcircuit pathways is especially critical for creating models that correctly predict the outcomes of candidate neurological therapies. Recent models are trying to specify how cortical circuits that enable planning and voluntary actions interact with adaptive subcortical microcircuits in the basal ganglia. The basal ganglia are strongly implicated in reinforcement learning, and in all behavior and cognition over which the frontal lobes exert flexible control. The persisting role of the basal ganglia shows that ancient vertebrate designs for motivated action selection proved adaptable enough to support many "modern" behavioral innovations, including fluent generation of language and speech. This paper summarizes how recent models have incorporated realistic representations of microcircuit features, and have begun to trace their computational implications. Also summarized are recent empirical discoveries that provide guidance regarding how to formulate the rules for synaptic modification that govern learning in cortico-striatal pathways. Such efforts are contributing to an emerging synthesis based on an interlocking set of computational hypotheses regarding cortical interactions with basal ganglia and thalamic nuclei. These hypotheses specify how specialized microcircuits solve learning and control problems inherent to the brain's parallel design.
Bullock
Daniel
D
Boston University, Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 02215, United States. danb@cns.bu.edu
Tan
Can Ozan
CO
John
Yohan J
YJ
D000109:Acetylcholine / Q000378:metabolism; D000679:Amygdala / Q000502:physiology; D001479:Basal Ganglia / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D003342:Corpus Striatum / Q000502:physiology; D004298:Dopamine / Q000378:metabolism; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology; D016548:Prosencephalon / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D012054:Reinforcement, Psychology; D013060:Speech / Q000502:physiology; D013569:Synapses / Q000502:physiology; D013788:Thalamus / Q000502:physiology; D013997:Time Factors; D035501:Uncertainty
D000109:Acetylcholine; D004298:Dopamine