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      Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making

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          Abstract

          Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity.

          Author summary

          Cognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an “upwards mapping” approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping “up” the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework.

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          Most cited references53

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          How spike generation mechanisms determine the neuronal response to fluctuating inputs.

          This study examines the ability of neurons to track temporally varying inputs, namely by investigating how the instantaneous firing rate of a neuron is modulated by a noisy input with a small sinusoidal component with frequency (f). Using numerical simulations of conductance-based neurons and analytical calculations of one-variable nonlinear integrate-and-fire neurons, we characterized the dependence of this modulation on f. For sufficiently high noise, the neuron acts as a low-pass filter. The modulation amplitude is approximately constant for frequencies up to a cutoff frequency, fc, after which it decays. The cutoff frequency increases almost linearly with the firing rate. For higher frequencies, the modulation amplitude decays as C/falpha, where the power alpha depends on the spike initiation mechanism. For conductance-based models, alpha = 1, and the prefactor C depends solely on the average firing rate and a spike "slope factor," which determines the sharpness of the spike initiation. These results are attributable to the fact that near threshold, the sodium activation variable can be approximated by an exponential function. Using this feature, we propose a simplified one-variable model, the "exponential integrate-and-fire neuron," as an approximation of a conductance-based model. We show that this model reproduces the dynamics of a simple conductance-based model extremely well. Our study shows how an intrinsic neuronal property (the characteristics of fast sodium channels) determines the speed with which neurons can track changes in input.
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            Microcircuitry of the direct and indirect pathways of the basal ganglia.

            Our understanding of the organization of the basal ganglia has advanced markedly over the last 10 years, mainly due to increased knowledge of their anatomical, neurochemical and physiological organization. These developments have led to a unifying model of the functional organization of the basal ganglia in both health and disease. The hypothesis is based on the so-called "direct" and "indirect" pathways of the flow of cortical information through the basal ganglia and has profoundly influenced the field of basal ganglia research, providing a framework for anatomical, physiological and clinical studies. The recent introduction of powerful techniques for the analysis of neuronal networks has led to further developments in our understanding of the basal ganglia. The objective of this commentary is to build upon the established model of the basal ganglia connectivity and review new anatomical findings that lead to the refinement of some aspects of the model. Four issues will be discussed. (1) The existence of several routes for the flow of cortical information along "indirect" pathways. (2) The synaptic convergence of information flowing through the "direct" and "indirect" pathways at the single-cell level in the basal ganglia output structures. (3) The convergence of functionally diverse information from the globus pallidus and the ventral pallidum at different levels of the basal ganglia. (4) The interconnections between the two divisions of the pallidal complex and the subthalamic nucleus and the characterization of the neuronal network underlying the indirect pathways. The findings summarized in this commentary confirm and elaborate the models of the direct and indirect pathways of information flow through the basal ganglia and provide a morphological framework for future studies.
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              Dopamine neurons encode the better option in rats deciding between differently delayed or sized rewards.

              The dopamine system is thought to be involved in making decisions about reward. Here we recorded from the ventral tegmental area in rats learning to choose between differently delayed and sized rewards. As expected, the activity of many putative dopamine neurons reflected reward prediction errors, changing when the value of the reward increased or decreased unexpectedly. During learning, neural responses to reward in these neurons waned and responses to cues that predicted reward emerged. Notably, this cue-evoked activity varied with size and delay. Moreover, when rats were given a choice between two differently valued outcomes, the activity of the neurons initially reflected the more valuable option, even when it was not subsequently selected.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Software
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                May 2019
                6 May 2019
                : 15
                : 5
                : e1006998
                Affiliations
                [1 ] Dept. of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
                [2 ] Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
                [3 ] Dept. de Matemàtiques i Informàtica, Universitat de les Illes Balears, Palma, Illes Balears, Spain
                [4 ] Institute of Applied Computing and Community Code, Palma, Illes Balears, Spain
                [5 ] Dept. of Biomedical Engineering, University of South Carolina, Columbia, South Carolina, United States of America
                [6 ] Dept. of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
                University of Oxford, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                ‡TV and JR also contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-7857-5133
                http://orcid.org/0000-0002-6305-9371
                http://orcid.org/0000-0001-6701-195X
                http://orcid.org/0000-0003-4720-0336
                http://orcid.org/0000-0002-1513-1551
                Article
                PCOMPBIOL-D-18-01740
                10.1371/journal.pcbi.1006998
                6534331
                31060045
                905b2fd2-769c-40b3-b35e-e7dcb68936b9
                © 2019 Dunovan et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 October 2018
                : 1 April 2019
                Page count
                Figures: 7, Tables: 4, Pages: 32
                Funding
                Funded by: Ministerio de Economía
                Award ID: MTM2014-54275-P
                Award Recipient :
                Funded by: Industria y Competitividad
                Award ID: MTM2015-71509-C2-2-R
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008530, European Regional Development Fund;
                Award ID: MTM2017-83568-P
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: DMS 1516288
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: CRCNS 1612913
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: CRCNS 1724240
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: CAREER award 1351748
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100006754, Army Research Laboratory;
                Award ID: W911NF-10-2-0022
                CV is supported by the Ministerio de Economía, Industria y Competitividad (MINECO), the Agencia Estatal de Investigación (AEI), and the European Regional Development Funds (ERDF) through projects MTM2014-54275-P, MTM2015-71509-C2-2-R and MTM2017-83568-P (AE/ERDF,EU). JR received support from NSF awards DMS 1516288, 1612913 (CRCNS), and 1724240 (CRCNS). TV received support from NSF CAREER award 1351748. The research was sponsored in part by the U.S. Army Research Laboratory, including work under Cooperative Agreement Number W911NF-10-2-0022, and the views espoused are not official policies of the U.S. Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                2019-05-24
                All data presented within the manuscript have been simulated and are not empirical measurements. All code used to run the simulations and create the figures may be found here: https://github.com/CoAxLab/CBGT.

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