Lateral orbitofrontal cortex anticipates choices and integrates prior with current information

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作者
Ramon Nogueira
Juan M. Abolafia
Jan Drugowitsch
Emili Balaguer-Ballester
Maria V. Sanchez-Vives
Rubén Moreno-Bote
机构
[1] Universitat Pompeu Fabra,Center for Brain and Cognition and Department of Information and Communications Technologies
[2] Research Unit,Département des Neurosciences Fondamentales
[3] Parc Sanitari Sant Joan de Déu,Department of Neurobiology
[4] Esplugues de Llobregat,Department of Computing and Informatics
[5] Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS),undefined
[6] Université de Genève,undefined
[7] Harvard Medical School,undefined
[8] Faculty of Science and Technology,undefined
[9] Bournemouth University,undefined
[10] Bernstein Center for Computational Neuroscience,undefined
[11] Central Institute of Mental Health,undefined
[12] Medical Faculty Mannheim/Heidelberg University,undefined
[13] ICREA,undefined
[14] Serra Húnter Fellow Programme,undefined
[15] Universitat Pompeu Fabra,undefined
来源
Nature Communications | / 8卷
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摘要
Adaptive behavior requires integrating prior with current information to anticipate upcoming events. Brain structures related to this computation should bring relevant signals from the recent past into the present. Here we report that rats can integrate the most recent prior information with sensory information, thereby improving behavior on a perceptual decision-making task with outcome-dependent past trial history. We find that anticipatory signals in the orbitofrontal cortex about upcoming choice increase over time and are even present before stimulus onset. These neuronal signals also represent the stimulus and relevant second-order combinations of past state variables. The encoding of choice, stimulus and second-order past state variables resides, up to movement onset, in overlapping populations. The neuronal representation of choice before stimulus onset and its build-up once the stimulus is presented suggest that orbitofrontal cortex plays a role in transforming immediate prior and stimulus information into choices using a compact state-space representation.
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