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Zhang X, Feng S, Yang X, Peng Y, Du M, Zhang R, Sima J, Zou F, Wu X, Wang Y, Gao X, Luo Y, Zhang M. Neuroelectrophysiological alteration associated with cognitive flexibility after 24 h sleep deprivation in adolescents. Conscious Cogn 2024; 124:103734. [PMID: 39096822 DOI: 10.1016/j.concog.2024.103734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 07/28/2024] [Accepted: 07/30/2024] [Indexed: 08/05/2024]
Abstract
The cognitive neural mechanisms by which sleep deprivation affects cognitive flexibility are poorly understood. Therefore, the study investigated the neuroelectrophysiological basis of the effect of 24 h sleep deprivation on cognitive flexibility in adolescents. 72 participants (36 females, mean age ± SD=20.46 ± 2.385 years old) participated in the study and were randomly assigned to the sleep deprivation group and control group. They were instructed to complete a task switch paradigm, during which participants' behavioral and electroencephalographic data were recorded. Behaviorally, there were significant between-group differences in accuracy. The results of event-related potential showed that the P2, N2 and P3 components had significant group effects or interaction effects. At the time-frequency level, there were statistically significant differences between the delta and theta bands. These results suggested that 24 h sleep deprivation affected problem-solving effectiveness rather than efficiency, mainly because it systematically impaired cognitive processing associated with cognitive flexibility.
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Affiliation(s)
- Xirui Zhang
- The First Affiliated Hospital of Xinxiang Medical University, Henan 453003, China
| | - Shuqing Feng
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Xiaochen Yang
- The First Affiliated Hospital of Xinxiang Medical University, Henan 453003, China
| | - Yunwen Peng
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Mei Du
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Rui Zhang
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Jiashan Sima
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Feng Zou
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Xin Wu
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Yufeng Wang
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China
| | - Xiaomeng Gao
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China.
| | - Yanyan Luo
- School of Nursing, Xinxiang Medical University, Henan 453003, China.
| | - Meng Zhang
- Department of Psychology, Xinxiang Medical University, Henan 453003, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang 453003, Henan Province, China.
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2
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Reppert TR, Heitz RP, Schall JD. Neural mechanisms for executive control of speed-accuracy trade-off. Cell Rep 2023; 42:113422. [PMID: 37950871 PMCID: PMC10833473 DOI: 10.1016/j.celrep.2023.113422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 08/23/2023] [Accepted: 10/27/2023] [Indexed: 11/13/2023] Open
Abstract
The medial frontal cortex (MFC) plays an important but disputed role in speed-accuracy trade-off (SAT). In samples of neural spiking in the supplementary eye field (SEF) in the MFC simultaneous with the visuomotor frontal eye field and superior colliculus in macaques performing a visual search with instructed SAT, during accuracy emphasis, most SEF neurons discharge less from before stimulus presentation until response generation. Discharge rates adjust immediately and simultaneously across structures upon SAT cue changes. SEF neurons signal choice errors with stronger and earlier activity during accuracy emphasis. Other neurons signal timing errors, covarying with adjusting response time. Spike correlations between neurons in the SEF and visuomotor areas did not appear, disappear, or change sign across SAT conditions or trial outcomes. These results clarify findings with noninvasive measures, complement previous neurophysiological findings, and endorse the role of the MFC as a critic for the actor instantiated in visuomotor structures.
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Affiliation(s)
- Thomas R Reppert
- Center for Integrative & Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA; Department of Psychology, The University of the South, Sewanee, TN 37383, USA
| | - Richard P Heitz
- Center for Integrative & Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA
| | - Jeffrey D Schall
- Center for Integrative & Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA; Centre for Vision Research, Vision Science to Applications, Department of Biology, York University, Toronto ON M3J 1P3, Canada.
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3
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Computational analysis of speed-accuracy tradeoff. Sci Rep 2022; 12:21995. [PMID: 36539428 PMCID: PMC9768160 DOI: 10.1038/s41598-022-26120-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Speed-accuracy tradeoff (SAT) in the decision making of humans and animals is a well-documented phenomenon, but its underlying neuronal mechanism remains unclear. Modeling approaches have conceptualized SAT through the threshold hypothesis as adjustments to the decision threshold. However, the leading neurophysiological view is the gain modulation hypothesis. This hypothesis postulates that the SAT mechanism is implemented through changes in the dynamics of the choice circuit, which increase the baseline firing rate and the speed of neuronal integration. In this paper, I investigated alternative computational mechanisms of SAT and showed that the threshold hypothesis was qualitatively consistent with the behavioral data, but the gain modulation hypothesis was not. In order to reconcile the threshold hypothesis with the neurophysiological evidence, I considered the interference of alpha oscillations with the decision process and showed that alpha oscillations could increase the discriminatory power of the decision system, although they slowed down the decision process. This suggests that the magnitude of alpha waves suppression during the event related desynchronization (ERD) of alpha oscillations depends on a SAT condition and the amplitude of alpha oscillations is lower in the speed condition. I also showed that the lower amplitude of alpha oscillations resulted in an increase in the baseline firing rate and the speed of neuronal intergration. Thus, the interference of the event related desynchronization of alpha oscillations with a SAT condition explains why an increase in the baseline firing rate and the speed of neuronal integration accompany the speed condition.
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4
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Umakantha A, Purcell BA, Palmeri TJ. Relating a Spiking Neural Network Model and the Diffusion Model of Decision-Making. COMPUTATIONAL BRAIN & BEHAVIOR 2022; 5:279-301. [PMID: 36408474 PMCID: PMC9673774 DOI: 10.1007/s42113-022-00143-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/26/2022] [Indexed: 06/16/2023]
Abstract
Many models of decision making assume accumulation of evidence to threshold as a core mechanism to predict response probabilities and response times. A spiking neural network model (Wang, 2002) instantiates these mechanisms at the level of biophysically-plausible pools of neurons with excitatory and inhibitory connections, and has numerous model parameters tuned by physiological measures. The diffusion model (Ratcliff, 1978) is a cognitive model that can be fitted to a range of behaviors and conditions. We investigated how parameters of the cognitive-level diffusion model relate to the parameters of a neural-level spiking model. In each simulated "experiment", we generated "data" from the spiking neural network by factorially combining a manipulation of choice difficulty (via the input to the spiking model) and a manipulation of one of the core parameters of the spiking model. We then fitted the diffusion model to these simulated data to observe how manipulation of each core spiking model parameter mapped on to fitted drift rate, response threshold, and non-decision time. Manipulations of parameters in the spiking model related to input sensitivity, threshold, and stimulus processing time mapped on to their conceptual analogues in the diffusion model, namely drift rate, threshold, and non-decision time. Manipulations of parameters in the spiking model with no direct analogue to the diffusion model, non-stimulus-specific background input, strength of recurrent excitation, and receptor conductances, mapped on to threshold in the diffusion model. We discuss implications of these results for interpretations of fits of the diffusion model to behavioral data.
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Affiliation(s)
- Akash Umakantha
- Neuroscience Institute, Carnegie Mellon University
- Machine Learning Department, Carnegie Mellon University
| | | | - Thomas J. Palmeri
- Psychology Department, Vanderbilt University
- Vanderbilt Vision Research Center, Vanderbilt University
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5
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Hernández-Navarro L, Hermoso-Mendizabal A, Duque D, de la Rocha J, Hyafil A. Proactive and reactive accumulation-to-bound processes compete during perceptual decisions. Nat Commun 2021; 12:7148. [PMID: 34880219 PMCID: PMC8655090 DOI: 10.1038/s41467-021-27302-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 11/03/2021] [Indexed: 11/09/2022] Open
Abstract
Standard models of perceptual decision-making postulate that a response is triggered in reaction to stimulus presentation when the accumulated stimulus evidence reaches a decision threshold. This framework excludes however the possibility that informed responses are generated proactively at a time independent of stimulus. Here, we find that, in a free reaction time auditory task in rats, reactive and proactive responses coexist, suggesting that choice selection and motor initiation, commonly viewed as serial processes, are decoupled in general. We capture this behavior by a novel model in which proactive and reactive responses are triggered whenever either of two competing processes, respectively Action Initiation or Evidence Accumulation, reaches a bound. In both types of response, the choice is ultimately informed by the Evidence Accumulation process. The Action Initiation process readily explains premature responses, contributes to urgency effects at long reaction times and mediates the slowing of the responses as animals get satiated and tired during sessions. Moreover, it successfully predicts reaction time distributions when the stimulus was either delayed, advanced or omitted. Overall, these results fundamentally extend standard models of evidence accumulation in decision making by showing that proactive and reactive processes compete for the generation of responses.
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Affiliation(s)
| | | | | | | | - Alexandre Hyafil
- Center for Brain and Cognition, Universitat Pompeu Fabra, Ramón Trias Fargas, 25, 08018, Barcelona, Spain.
- Center of Mathematical Research, Campus UAB Edifici C, 08193, Bellaterra (Barcelona), Spain.
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6
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Guan Q, Wang J, Chen Y, Liu Y, He H. Beyond information rate, the capacity of cognitive control predicts response criteria in perceptual decision-making. Brain Cogn 2021; 154:105788. [PMID: 34481205 DOI: 10.1016/j.bandc.2021.105788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 08/10/2021] [Accepted: 08/23/2021] [Indexed: 10/20/2022]
Abstract
Recent studies indicate that higher capacity of cognitive control (CCC) represents higher processing efficiency (i.e., high accuracy with fast speed). However, the speed-accuracy tradeoff (SAT) exists ubiquitously in decision-making, and little is known about whether and how the CCC is associated with SAT and whether the CCC-SAT relationship would be affected by changes in information entropy. In this study, fifty-nine college students performed a majority function task in which accuracy and response speed were equally emphasized. A Bayesian-based hierarchical drift diffusion modeling method was used to estimate three parameters of boundary separation, drift rate, and nondecision time for each participant in this task. In addition, the CCC of each participant was estimated. The results showed that the CCC was positively correlated with the SAT represented by jointly increasing accuracy and reaction time (RT), which was modulated by the change in task-relevant information entropy. Multiple mediation analyses indicated that drift rate served as the key mediator in the positive CCC-accuracy relationship while boundary separation played the major mediating role in the positive CCC-RT relationship. These findings suggest that the CCC reflects not only the rate of information processing but also decision strategies for achieving current goals.
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Affiliation(s)
- Qing Guan
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China; Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China; Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China
| | - Jing Wang
- Sichuan Provincial Center for Mental Health, Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yiqi Chen
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
| | - Ying Liu
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
| | - Hao He
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China; Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China.
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7
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Schall JD, Paré M. The unknown but knowable relationship between Presaccadic Accumulation of activity and Saccade initiation. J Comput Neurosci 2021; 49:213-228. [PMID: 33712942 DOI: 10.1007/s10827-021-00784-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 01/06/2021] [Accepted: 02/16/2021] [Indexed: 12/01/2022]
Abstract
The goal of this short review is to call attention to a yawning gap of knowledge that separates two processes essential for saccade production. On the one hand, knowledge about the saccade generation circuitry within the brainstem is detailed and precise - push-pull interactions between gaze-shifting and gaze-holding processes control the time of saccade initiation, which begins when omnipause neurons are inhibited and brainstem burst neurons are excited. On the other hand, knowledge about the cortical and subcortical premotor circuitry accomplishing saccade initiation has crystalized around the concept of stochastic accumulation - the accumulating activity of saccade neurons reaching a fixed value triggers a saccade. Here is the gap: we do not know how the reaching of a threshold by premotor neurons causes the critical pause and burst of brainstem neurons that initiates saccades. Why this problem matters and how it can be addressed will be discussed. Closing the gap would unify two rich but curiously disconnected empirical and theoretical domains.
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Affiliation(s)
- Jeffrey D Schall
- Centre for Vision Research, Vision Science to Application, Department of Biology, York University, Ontario, M3J 1P3, Toronto, Canada.
| | - Martin Paré
- Department of Biomedical & Molecular Sciences and of Psychology, Queen's University, Ontario, ON K7L 3N6, Kingston, Canada
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8
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Capogna M, Castillo PE, Maffei A. The ins and outs of inhibitory synaptic plasticity: Neuron types, molecular mechanisms and functional roles. Eur J Neurosci 2020; 54:6882-6901. [PMID: 32663353 DOI: 10.1111/ejn.14907] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 06/30/2020] [Accepted: 07/08/2020] [Indexed: 01/05/2023]
Abstract
GABAergic interneurons are highly diverse, and their synaptic outputs express various forms of plasticity. Compelling evidence indicates that activity-dependent changes of inhibitory synaptic transmission play a significant role in regulating neural circuits critically involved in learning and memory and circuit refinement. Here, we provide an updated overview of inhibitory synaptic plasticity with a focus on the hippocampus and neocortex. To illustrate the diversity of inhibitory interneurons, we discuss the case of two highly divergent interneuron types, parvalbumin-expressing basket cells and neurogliaform cells, which support unique roles on circuit dynamics. We also present recent progress on the molecular mechanisms underlying long-term, activity-dependent plasticity of fast inhibitory transmission. Lastly, we discuss the role of inhibitory synaptic plasticity in neuronal circuits' function. The emerging picture is that inhibitory synaptic transmission in the CNS is extremely diverse, undergoes various mechanistically distinct forms of plasticity and contributes to a much more refined computational role than initially thought. Both the remarkable diversity of inhibitory interneurons and the various forms of plasticity expressed by GABAergic synapses provide an amazingly rich inhibitory repertoire that is central to a variety of complex neural circuit functions, including memory.
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Affiliation(s)
- Marco Capogna
- Department of Biomedicine, Danish National Research Foundation Center of Excellence PROMEMO, Aarhus University, Aarhus, Denmark
| | - Pablo E Castillo
- Dominck P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Arianna Maffei
- Center for Neural Circuit Dynamics and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, USA
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9
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Kleinloog JPD, Mensink RP, Ivanov D, Adam JJ, Uludağ K, Joris PJ. Aerobic Exercise Training Improves Cerebral Blood Flow and Executive Function: A Randomized, Controlled Cross-Over Trial in Sedentary Older Men. Front Aging Neurosci 2019; 11:333. [PMID: 31866855 PMCID: PMC6904365 DOI: 10.3389/fnagi.2019.00333] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 11/19/2019] [Indexed: 12/31/2022] Open
Abstract
Background Physical activity may attenuate age-related cognitive decline by improving cerebrovascular function. The aim of this study was therefore to investigate effects of aerobic exercise training on cerebral blood flow (CBF), which is a sensitive physiological marker of cerebrovascular function, in sedentary older men. Methods Seventeen apparently healthy men, aged 60–70 years and with a BMI between 25 and 35 kg/m2, were included in a randomized, controlled cross-over trial. Study participants were randomly allocated to a fully-supervised, progressive, aerobic exercise training or no-exercise control period for 8 weeks, separated by a 12-week wash-out period. Measurements at the end of each period included aerobic fitness evaluated using peak oxygen consumption during incremental exercise (VO2peak), CBF measured with pseudo-continuous arterial spin labeling magnetic resonance imaging, and post-load glucose responses determined using an oral glucose tolerance test (OGTT). Furthermore, cognitive performance was assessed in the domains of executive function, memory, and psychomotor speed. Results VO2peak significantly increased following aerobic exercise training compared to no-exercise control by 262 ± 236 mL (P < 0.001). CBF was increased by 27% bilaterally in the frontal lobe, particularly the subcallosal and anterior cingulate gyrus (cluster volume: 1008 mm3; P < 0.05), while CBF was reduced by 19% in the right medial temporal lobe, mainly temporal fusiform gyrus (cluster volume: 408 mm3; P < 0.05). Mean post-load glucose concentrations determined using an OGTT decreased by 0.33 ± 0.63 mmol/L (P = 0.049). Furthermore, executive function improved as the latency of response was reduced by 5% (P = 0.034), but no changes were observed in memory or psychomotor speed. Conclusion Aerobic exercise training improves regional CBF in sedentary older men. These changes in CBF may underlie exercise-induced beneficial effects on executive function, which could be partly mediated by improvements in glucose metabolism. This clinical trial is registered on ClinicalTrials.gov as NCT03272061.
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Affiliation(s)
- Jordi P D Kleinloog
- Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Ronald P Mensink
- Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Jos J Adam
- Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Kamil Uludağ
- Department of Biomedical Engineering, N Center, Sungkyunkwan University, Suwon, South Korea.,Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, ON, Canada
| | - Peter J Joris
- Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
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10
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Servant M, Tillman G, Schall JD, Logan GD, Palmeri TJ. Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence. J Neurophysiol 2019; 121:1300-1314. [PMID: 30726163 PMCID: PMC6485731 DOI: 10.1152/jn.00507.2018] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 02/01/2019] [Accepted: 02/02/2019] [Indexed: 11/22/2022] Open
Abstract
Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.
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Affiliation(s)
- Mathieu Servant
- Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University , Nashville, Tennessee
| | - Gabriel Tillman
- Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University , Nashville, Tennessee
| | - Jeffrey D Schall
- Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University , Nashville, Tennessee
| | - Gordon D Logan
- Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University , Nashville, Tennessee
| | - Thomas J Palmeri
- Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University , Nashville, Tennessee
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11
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Huang YC, Wang CT, Su TS, Kao KW, Lin YJ, Chuang CC, Chiang AS, Lo CC. A Single-Cell Level and Connectome-Derived Computational Model of the Drosophila Brain. Front Neuroinform 2019; 12:99. [PMID: 30687056 PMCID: PMC6335393 DOI: 10.3389/fninf.2018.00099] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 12/10/2018] [Indexed: 12/04/2022] Open
Abstract
Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which (1) identifies the polarity of each neuron arbor, (2) predicts connections between neurons, (3) translates morphology data from the database into physiology parameters for computational modeling, (4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and (5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.
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Affiliation(s)
- Yu-Chi Huang
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan.,Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan
| | - Cheng-Te Wang
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan.,Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan
| | - Ta-Shun Su
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan.,Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan
| | - Kuo-Wei Kao
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan
| | - Yen-Jen Lin
- Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan.,National Center for High-Performance Computing, Hsinchu, Taiwan
| | | | - Ann-Shyn Chiang
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan.,Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan.,Institute of Physics, Academia Sinica, Nankang, Taiwan.,Institute of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan, Taiwan.,Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, United States
| | - Chung-Chuan Lo
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan.,Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan
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12
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Fan Y, Gold JI, Ding L. Ongoing, rational calibration of reward-driven perceptual biases. eLife 2018; 7:e36018. [PMID: 30303484 PMCID: PMC6203438 DOI: 10.7554/elife.36018] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 10/07/2018] [Indexed: 11/13/2022] Open
Abstract
Decision-making is often interpreted in terms of normative computations that maximize a particular reward function for stable, average behaviors. Aberrations from the reward-maximizing solutions, either across subjects or across different sessions for the same subject, are often interpreted as reflecting poor learning or physical limitations. Here we show that such aberrations may instead reflect the involvement of additional satisficing and heuristic principles. For an asymmetric-reward perceptual decision-making task, three monkeys produced adaptive biases in response to changes in reward asymmetries and perceptual sensitivity. Their choices and response times were consistent with a normative accumulate-to-bound process. However, their context-dependent adjustments to this process deviated slightly but systematically from the reward-maximizing solutions. These adjustments were instead consistent with a rational process to find satisficing solutions based on the gradient of each monkey's reward-rate function. These results suggest new dimensions for assessing the rational and idiosyncratic aspects of flexible decision-making.
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Affiliation(s)
- Yunshu Fan
- Neuroscience Graduate Group, Department of NeuroscienceUniversity of PennsylvaniaPhiladelphiaUnited States
| | - Joshua I Gold
- Neuroscience Graduate Group, Department of NeuroscienceUniversity of PennsylvaniaPhiladelphiaUnited States
| | - Long Ding
- Neuroscience Graduate Group, Department of NeuroscienceUniversity of PennsylvaniaPhiladelphiaUnited States
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Neural Evidence for a Role of Urgency in the Speed-Accuracy Trade-off in Perceptual Decision-Making. J Neurosci 2018; 36:5909-10. [PMID: 27251612 DOI: 10.1523/jneurosci.0894-16.2016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 04/26/2016] [Indexed: 11/21/2022] Open
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Working Memory and Decision-Making in a Frontoparietal Circuit Model. J Neurosci 2017; 37:12167-12186. [PMID: 29114071 DOI: 10.1523/jneurosci.0343-17.2017] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 08/24/2017] [Accepted: 09/19/2017] [Indexed: 12/25/2022] Open
Abstract
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.SIGNIFICANCE STATEMENT Working memory and decision-making are fundamental "building blocks" 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.
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Su TS, Lee WJ, Huang YC, Wang CT, Lo CC. Coupled symmetric and asymmetric circuits underlying spatial orientation in fruit flies. Nat Commun 2017; 8:139. [PMID: 28747622 PMCID: PMC5529380 DOI: 10.1038/s41467-017-00191-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 06/08/2017] [Indexed: 11/13/2022] Open
Abstract
Maintaining spatial orientation when carrying out goal-directed movements requires an animal to perform angular path integration. Such functionality has been recently demonstrated in the ellipsoid body (EB) of fruit flies, though the precise circuitry and underlying mechanisms remain unclear. We analyze recently published cellular-level connectomic data and identify the unique characteristics of the EB circuitry, which features coupled symmetric and asymmetric rings. By constructing a spiking neural circuit model based on the connectome, we reveal that the symmetric ring initiates a feedback circuit that sustains persistent neural activity to encode information regarding spatial orientation, while the asymmetric rings are capable of integrating the angular path when the body rotates in the dark. The present model reproduces several key features of EB activity and makes experimentally testable predictions, providing new insight into how spatial orientation is maintained and tracked at the cellular level. Ellipsoid body (EB) neurons in the fruit fly represent the animal heading through a bump-like activity dynamics. Here the authors report a connectome-driven spiking neural circuit model of the EB and the protocerebral bridge (PB) that can maintain and update an activity bump related to the spatial orientation.
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Affiliation(s)
- Ta-Shun Su
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Wan-Ju Lee
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Yu-Chi Huang
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan.,Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Cheng-Te Wang
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan.,Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Chung-Chuan Lo
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan. .,Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 30013, Taiwan. .,Brain Research Center, National Tsing Hua University, Hsinchu, 30013, Taiwan.
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Purcell BA, Palmeri TJ. RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2017; 76:156-171. [PMID: 28392584 PMCID: PMC5381950 DOI: 10.1016/j.jmp.2016.07.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception.
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Purcell BA, Kiani R. Neural Mechanisms of Post-error Adjustments of Decision Policy in Parietal Cortex. Neuron 2016; 89:658-71. [PMID: 26804992 PMCID: PMC4742416 DOI: 10.1016/j.neuron.2015.12.027] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 09/21/2015] [Accepted: 12/15/2015] [Indexed: 10/22/2022]
Abstract
Humans often slow down after mistakes (post-error slowing [PES]), but the neural mechanism and adaptive role of PES remain controversial. We studied changes in the neural mechanisms of decision making after errors in humans and monkeys that performed a motion direction discrimination task. We found that PES is mediated by two factors: a reduction in sensitivity to sensory information and an increase in the decision bound. Both effects are implemented through dynamic changes in the decision-making process. Neuronal responses in the monkey lateral intraparietal area revealed that bound changes are implemented by decreasing an evidence-independent urgency signal. They also revealed a reduction in the rate of evidence accumulation, reflecting reduced sensitivity. These changes in the bound and sensitivity provide a quantitative account of choices and response times. We suggest that PES reflects an adaptive increase of decision bound in anticipation of maladaptive reductions in sensitivity to incoming evidence.
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Affiliation(s)
- Braden A Purcell
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY 10003, USA.
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Neural dynamics and circuit mechanisms of decision-making. Curr Opin Neurobiol 2012; 22:1039-46. [PMID: 23026743 DOI: 10.1016/j.conb.2012.08.006] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2012] [Revised: 08/19/2012] [Accepted: 08/21/2012] [Indexed: 11/24/2022]
Abstract
In this review, I briefly summarize current neurobiological studies of decision-making that bear on two general themes. The first focuses on the nature of neural representation and dynamics in a decision circuit. Experimental and computational results suggest that ramping-to-threshold in the temporal domain and trajectory of population activity in the state space represent a duality of perspectives on a decision process. Moreover, a decision circuit can display several different dynamical regimes, such as the ramping mode and the jumping mode with distinct defining properties. The second is concerned with the relationship between biologically-based mechanistic models and normative-type models. A fruitful interplay between experiments and these models at different levels of abstraction have enabled investigators to pose increasingly refined questions and gain new insights into the neural basis of decision-making. In particular, recent work on multi-alternative decisions suggests that deviations from rational models of choice behavior can be explained by established neural mechanisms.
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