{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T09:27:30Z","timestamp":1751880450754},"reference-count":75,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2015,9]]},"abstract":"<jats:p>There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input\u2013output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter\u2013programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of \"recycled\" neurons (interpreter). The PFC thus acts as a \"programmer\" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the \"programmer\u2013interpreter\" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.<\/jats:p>","DOI":"10.1142\/s0129065715500173","type":"journal-article","created":{"date-parts":[[2015,3,13]],"date-time":"2015-03-13T01:19:09Z","timestamp":1426209549000},"page":"1550017","source":"Crossref","is-referenced-by-count":18,"title":["A Programmer\u2013Interpreter Neural Network Architecture for Prefrontal Cognitive Control"],"prefix":"10.1142","volume":"25","author":[{"given":"Francesco","family":"Donnarumma","sequence":"first","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Via S. Martino della Battaglia 44-00185 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Prevete","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Napoli Federico II, Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione (DIETI), Via Claudio, 21, 80125 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Chersi","sequence":"additional","affiliation":[{"name":"University College London, Institute of Cognitive Neuroscience, 17 Queen Square, London, WC1N 3AR, England"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"Pezzulo","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Via S. 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