{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T23:36:47Z","timestamp":1794440207197,"version":"4.9.1"},"reference-count":117,"publisher":"Society for Neuroscience","issue":"50","license":[{"start":{"date-parts":[[2018,6,13]],"date-time":"2018-06-13T00:00:00Z","timestamp":1528848000000},"content-version":"vor","delay-in-days":218,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Neurosci."],"published-print":{"date-parts":[[2017,12,13]]},"abstract":"<jats:p>Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and distinct roles of these areas and the nature of their coordination in cognitive function remain poorly understood. Biophysically based computational models of cortical circuits have provided insights into the mechanisms supporting these functions, yet they have primarily focused on the local microcircuit level, raising questions about the principles for distributed cognitive computation in multiregional networks. To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits. The circuit architecture includes hierarchical differences in local recurrent structure and implements reciprocal long-range projections. This parsimonious model captures a range of behavioral and neuronal features of frontoparietal circuits across multiple WM and DM paradigms. In the context of WM, both areas exhibit persistent activity, but, in response to intervening distractors, PPC transiently encodes distractors while PFC filters distractors and supports WM robustness. With regard to DM, the PPC module generates graded representations of accumulated evidence supporting target selection, while the PFC module generates more categorical responses related to action or choice. These findings suggest computational principles for distributed, hierarchical processing in cortex during cognitive function and provide a framework for extension to multiregional models.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>SIGNIFICANCE STATEMENT<\/jats:bold>\n                    Working memory and decision-making are fundamental \u201cbuilding blocks\u201d of cognition, and deficits in these functions are associated with neuropsychiatric disorders such as schizophrenia. These cognitive functions engage distributed networks with prefrontal cortex (PFC) and posterior parietal cortex (PPC) at the core. It is not clear, however, what the contributions of PPC and PFC are in light of the computations that subserve working memory and decision-making. We constructed a biophysical model of a reciprocally connected frontoparietal circuit that revealed shared and distinct functions for the PFC and PPC across working memory and decision-making tasks. Our parsimonious model connects circuit-level properties to cognitive functions and suggests novel design principles beyond those of local circuits for cognitive processing in multiregional brain networks.\n                  <\/jats:p>","DOI":"10.1523\/jneurosci.0343-17.2017","type":"journal-article","created":{"date-parts":[[2017,12,13]],"date-time":"2017-12-13T12:55:32Z","timestamp":1513169732000},"page":"12167-12186","source":"Crossref","is-referenced-by-count":197,"title":["Working Memory and Decision-Making in a Frontoparietal Circuit Model"],"prefix":"10.1523","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4115-8181","authenticated-orcid":false,"given":"John 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