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      Cortical Microcircuitry of Performance Monitoring

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      Nature neuroscience

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          Abstract

          Medial frontal cortex enables performance monitoring, indexed by the error-related negativity (ERN) and manifest by performance adaptations. In monkeys performing a saccade countermanding (stop signal) task, we recorded EEG over and neural spiking across all layers of the supplementary eye field (SEF), an agranular cortical area. Neurons signaling error production, feedback predicting reward gain or loss, and delivery of fluid reward had different spike widths and were concentrated differently across layers. Neurons signaling error or loss of reward were more common in layers 2 and 3 (L2/3), while neurons signaling gain of reward were more common in layers 5 and 6 (L5/6). Variation of error- and reinforcement-related spike rates in L2/3 but not L5/6 predicted response time adaptation. Variation in error-related spike rate in L2/3 but not L5/6 predicted ERN magnitude. These findings reveal novel features of cortical microcircuitry supporting performance monitoring and confirm one cortical source of the ERN.

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          Most cited references41

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          Receptive fields and functional architecture of monkey striate cortex.

          1. The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light. Most cells can be categorized as simple, complex, or hypercomplex, with response properties very similar to those previously described in the cat. On the average, however, receptive fields are smaller, and there is a greater sensitivity to changes in stimulus orientation. A small proportion of the cells are colour coded.2. Evidence is presented for at least two independent systems of columns extending vertically from surface to white matter. Columns of the first type contain cells with common receptive-field orientations. They are similar to the orientation columns described in the cat, but are probably smaller in cross-sectional area. In the second system cells are aggregated into columns according to eye preference. The ocular dominance columns are larger than the orientation columns, and the two sets of boundaries seem to be independent.3. There is a tendency for cells to be grouped according to symmetry of responses to movement; in some regions the cells respond equally well to the two opposite directions of movement of a line, but other regions contain a mixture of cells favouring one direction and cells favouring the other.4. A horizontal organization corresponding to the cortical layering can also be discerned. The upper layers (II and the upper two-thirds of III) contain complex and hypercomplex cells, but simple cells are virtually absent. The cells are mostly binocularly driven. Simple cells are found deep in layer III, and in IV A and IV B. In layer IV B they form a large proportion of the population, whereas complex cells are rare. In layers IV A and IV B one finds units lacking orientation specificity; it is not clear whether these are cell bodies or axons of geniculate cells. In layer IV most cells are driven by one eye only; this layer consists of a mosaic with cells of some regions responding to one eye only, those of other regions responding to the other eye. Layers V and VI contain mostly complex and hypercomplex cells, binocularly driven.5. The cortex is seen as a system organized vertically and horizontally in entirely different ways. In the vertical system (in which cells lying along a vertical line in the cortex have common features) stimulus dimensions such as retinal position, line orientation, ocular dominance, and perhaps directionality of movement, are mapped in sets of superimposed but independent mosaics. The horizontal system segregates cells in layers by hierarchical orders, the lowest orders (simple cells monocularly driven) located in and near layer IV, the higher orders in the upper and lower layers.
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            Distinct roles for direct and indirect pathway striatal neurons in reinforcement

            Dopamine signaling is implicated in reinforcement learning, but the neural substrates targeted by dopamine are poorly understood. Here, we bypassed dopamine signaling itself and tested how optogenetic activation of dopamine D1- or D2-receptor-expressing striatal projection neurons influenced reinforcement learning in mice. Stimulating D1-expressing neurons induced persistent reinforcement, whereas stimulating D2-expressing neurons induced transient punishment, demonstrating that activation of these circuits is sufficient to modify the probability of performing future actions.
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              Models of response inhibition in the stop-signal and stop-change paradigms.

              The stop-signal paradigm is very useful for the study of response inhibition. Stop-signal performance is typically described as a race between a go process, triggered by a go stimulus, and a stop process, triggered by the stop signal. Response inhibition depends on the relative finishing time of these two processes. Numerous studies have shown that the independent horse-race model of Logan and Cowan [Logan, G.D., Cowan, W.B., 1984. On the ability to inhibit thought and action: a theory of an act of control. Psychological Review 91, 295-327] accounts for the data very well. In the present article, we review the independent horse-race model and related models, such as the interactive horse-race model [Boucher, L., Palmeri, T.J., Logan, G.D., Schall, J.D., 2007. Inhibitory control in mind and brain: an interactive race model of countermanding saccades. Psychological Review 114, 376-397]. We present evidence that favors the independent horse-race model but also some evidence that challenges the model. We end with a discussion of recent models that elaborate the role of a stop process in inhibiting a response.
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                28 November 2018
                14 January 2019
                February 2019
                14 July 2019
                : 22
                : 2
                : 265-274
                Affiliations
                Department of Psychology, Vanderbilt Vision Research Center, Center for Integrative & Cognitive Neuroscience, Vanderbilt University, Nashville, TN
                Author notes

                Author contributions

                Experimental design, D.C.G. and J.D.S. Data collection, D.C.G. Data analysis, A.S. Interpretation and preparation of manuscript, A.S., D.C.G., and J.D.S.

                Corresponding author: Jeffrey D. Schall, Ph.D. jeffrey.d.schall@ 123456vanderbilt.edu
                Article
                NIHMS1514960
                10.1038/s41593-018-0309-8
                6348027
                30643297
                1915f904-9574-4fef-ba5a-9e383f51e1de

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms)

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