📱 حمّل التطبيق
من لبنان لكل لبنان
اخبار الموقع
ليلة غنائية لإليسا في كاليفورنيا 24 يناير 2027 وسط حضور الجاليات العربيةبعد 23 يومًا من وفاة شاب رفض نقل الدم.. «شهود يهوه» يعدّلون قواعدهمبالازو فيرساتشي دبي يعزز حضوره بالفوز بجائزة صاحب عمل في دول مجلس التعاون 2026كريم نور يختتم جولته بأمسية حيوية في هيوستن ويستعد للقاء الجمهور ببيروتغودزيلا يكتسح شباك التذاكر العالمي.. وملحمة «رامايانا» تشعل المنافسة السينمائية بإيرادات ضخمةالصحة العالمية تحذر: 6 إجراءات لمواجهة تغير المناخ قد تنقذ 1.6 مليون حياة سنوياًثورة جديدة في التكنولوجيا.. روبوت ينجح لأول مرة في بناء هياكل داخل الفضاء وغوغل تطلق جيلاً جديداً من رقائق الذكاء الاصطناعياتحاد الجزائر يهزّ إفريقيا ويتوج بالسوبر الأفريقي على حساب صن داونز بثلاثية تاريخية
العلوم و التكنولوجيا

Prefrontal neural geometry of learned cues guides motivated behaviours

Prefrontal neural geometry of learned cues guides motivated behaviours

  • Pearce, J. M. & Bouton, M. E. Theories of associative learning in animals. Annu. Rev. Psychol. 52, 111–139 (2001).

    Article 
    PubMed 

    Google Scholar 

  • Rolls, E. T. What are emotional states, and why do we have them? Emot. Rev. 5, 241–247 (2013).

    Article 

    Google Scholar 

  • O’Doherty, J. P. The problem with value. Neurosci. Biobehav. Rev. 43, 259–268 (2014).

  • Tye, K. M. Neural circuit motifs in valence processing. Neuron 100, 436–452 (2018).

  • Kahnt, T., Park, S. Q., Haynes, J. D. & Tobler, P. N. Disentangling neural representations of value and salience in the human brain. Proc. Natl Acad. Sci. USA 111, 5000–5005 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rangel, A., Camerer, C. & Montague, P. R. A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).

  • Rolls, E. T. Emotion Explained (Oxford Academic, 2009).

  • Paton, J. J., Belova, M. A., Morrison, S. E. & Salzman, C. D. The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature 439, 865–870 (2006).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Morrison, S. E. & Salzman, C. D. Re-valuing the amygdala. Curr. Opin. Neurobiol. 20, 221–230 (2010).

  • Rudebeck, P. H. & Murray, E. A. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84, 1143–1156 (2014).

  • Hinz, J. et al. Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice. Nat. Commun. 16, 5239 (2025).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kahnt, T. & Tobler, P. N. in Decision Neuroscience: An Integrative Perspective (eds Dreher, J.-C. & Tremblay, L.) Ch. 9 (Elsevier, 2017).

  • Dalley, J. W., Cardinal, R. N. & Robbins, T. W. Prefrontal executive and cognitive functions in rodents: neural and neurochemical substrates. Neurosci. Biobehav. Rev. 28, 771–784 (2004).

  • Sotres-Bayon, F. & Quirk, G. J. Prefrontal control of fear: more than just extinction. Curr. Opin. Neurobiol. 20, 231–235 (2010).

  • Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Moorman, D. E. & Aston-Jones, G. Prefrontal neurons encode context-based response execution and inhibition in reward seeking and extinction. Proc. Natl Acad. Sci. USA 112, 9472–9477 (2015).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kim, C. K. et al. Molecular and circuit-dynamical identification of top-down neural mechanisms for restraint of reward seeking. Cell 170, 1013–1027 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Otis, J. M. et al. Prefrontal cortex output circuits guide reward seeking through divergent cue encoding. Nature 543, 103–107 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Corcoran, K. A. & Quirk, G. J. Activity in prelimbic cortex is necessary for the expression of learned, but not innate, fears. J. Neurosci. 27, 840–844 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Courtin, J. et al. Prefrontal parvalbumin interneurons shape neuronal activity to drive fear expression. Nature 505, 92–96 (2014).

    Article 
    ADS 
    PubMed 

    Google Scholar 

  • Bravo-Rivera, C., Roman-Ortiz, C., Brignoni-Perez, E., Sotres-Bayon, F. & Quirk, G. J. Neural structures mediating expression and extinction of platform-mediated avoidance. J. Neurosci. 34, 9736–9742 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jercog, D. et al. Dynamical prefrontal population coding during defensive behaviours. Nature 595, 690–694 (2021).

    Article 
    ADS 
    PubMed 

    Google Scholar 

  • Hok, V., Save, E., Lenck-Santini, P. P. & Poucet, B. Coding for spatial goals in the prelimbic/infralimbic area of the rat frontal cortex. Proc. Natl Acad. Sci. USA 102, 4602–4607 (2005).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Campus, P. et al. The paraventricular thalamus is a critical mediator of top-down control of cue-motivated behavior in rats. eLife 8, e49041 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Choi, E. A. et al. A corticothalamic circuit trades off speed for safety during decision-making under motivational conflict. J. Neurosci. 42, 3473–3483 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kietzman, H. W., Trinoskey-Rice, G., Blumenthal, S. A., Guo, J. D. & Gourley, S. L. Social incentivization of instrumental choice in mice requires amygdala-prelimbic cortex-nucleus accumbens connectivity. Nat. Commun. 13, 4768 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Burgos-Robles, A. et al. Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment. Nat. Neurosci. 20, 824–835 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Caracheo, B. F., Grewal, J. J. S. & Seamans, J. K. Persistent valence representations by ensembles of anterior cingulate cortex neurons. Front. Syst. Neurosci. 12, 51 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vander Weele, C. M. et al. Dopamine enhances signal-to-noise ratio in cortical-brainstem encoding of aversive stimuli. Nature 563, 397–401 (2018).

    Article 
    ADS 

    Google Scholar 

  • Kyriazi, P., Headley, D. B. & Paré, D. Different multidimensional representations across the amygdalo-prefrontal network during an approach-avoidance task. Neuron 107, 717–730 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Huang, W. C., Zucca, A., Levy, J. & Page, D. T. Social behavior is modulated by valence-encoding mPFC-amygdala sub-circuitry. Cell Rep. 32, 107899 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yu, Y. H. et al. Optogenetic stimulation in the medial prefrontal cortex modulates stimulus valence from rewarding and aversive to neutral states. Front. Psychiatry 14, 1119803 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Allen, W. E. et al. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364, eaav3932 (2019).

    Article 

    Google Scholar 

  • Wang, P. Y. et al. Transient and persistent representations of odor value in prefrontal cortex. Neuron 108, 209–224 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kondo, M. & Matsuzaki, M. Neuronal representations of reward-predicting cues and outcome history with movement in the frontal cortex. Cell Rep. 34, 108704 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Ottenheimer, D. J., Hjort, M. M., Bowen, A. J., Steinmetz, N. A. & Stuber, G. D. A stable, distributed code for cue value in mouse cortex during reward learning. eLife 12, RP84604 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed coding of choice, action and engagement across the mouse brain. Nature 576, 266–273 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Litt, A., Plassmann, H., Shiv, B. & Rangel, A. Dissociating valuation and saliency signals during decision-making. Cereb. Cortex 21, 95–102 (2011).

    Article 
    PubMed 

    Google Scholar 

  • Zhang, Z. et al. Distributed neural representation of saliency controlled value and category during anticipation of rewards and punishments. Nat. Commun. 8, 1907 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rolls, E. T. Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala. Brain Struct. Funct. 228, 1201–1257 (2023).

  • Stephenson-Jones, M. et al. A basal ganglia circuit for evaluating action outcomes. Nature 539, 289–293 (2016).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Stephenson-Jones, M. et al. Opposing contributions of GABAergic and glutamatergic ventral pallidal neurons to motivational behaviors. Neuron 105, 921–933 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wallis, J. D. Decoding cognitive processes from neural ensembles. Trends Cogn. Sci. 22, 1091–1102 (2018).

  • Quirk, G. J. & Mueller, D. Neural mechanisms of extinction learning and retrieval. Neuropsychopharmacology 33, 56–72 (2008).

  • Grewe, B. F. et al. Neural ensemble dynamics underlying a long-term associative memory. Nature 543, 670–675 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).

    Article 

    Google Scholar 

  • Bissonette, G. B. et al. Separate populations of neurons in ventral striatum encode value and motivation. PLoS ONE 8, e64673 (2013).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Beyeler, A. et al. Divergent routing of positive and negative information from the amygdala during memory retrieval. Neuron 90, 348–361 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, X. & Li, B. Population coding of valence in the basolateral amygdala. Nat. Commun. 9, 5195 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lutas, A. et al. State-specific gating of salient cues by midbrain dopaminergic input to basal amygdala. Nat. Neurosci. 22, 1820–1833 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Roelofs, K. & Dayan, P. Freezing revisited: coordinated autonomic and central optimization of threat coping. Nat. Rev. Neurosci. 23, 568–580 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Szeska, C., Richter, J., Wendt, J., Weymar, M. & Hamm, A. O. Attentive immobility in the face of inevitable distal threat—startle potentiation and fear bradycardia as an index of emotion and attention. Psychophysiology 58, e13812 (2021).

    Article 
    PubMed 

    Google Scholar 

  • van Heukelum, S. et al. Where is cingulate cortex? A cross-species view. Trends Neurosci. 43, 285–299 (2020).

  • Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bernardi, S. et al. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183, 954–967 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Flesch, T., Juechems, K., Dumbalska, T., Saxe, A. & Summerfield, C. Orthogonal representations for robust context-dependent task performance in brains and neural networks. Neuron 110, 4212–4219 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fortunato, C. et al. Nonlinear manifolds underlie neural population activity during behaviour. Preprint at bioRxiv https://doi.org/10.1101/2023.07.18.549575 (2024).

  • Rozeske, R. R. et al. Prefrontal neuronal circuits of contextual fear conditioning. Genes Brain Behav. 14, 22–36 (2015).

  • Del Arco, A., Park, J. & Moghaddam, B. Unanticipated stressful and rewarding experiences engage the same prefrontal cortex and ventral tegmental area neuronal populations. eNeuro https://doi.org/10.1523/ENEURO.0029-20.2020 (2020).

  • Rozeske, R. R., Valerio, S., Chaudun, F. & Herry, C. Prefrontal neuronal circuits of contextual fear conditioning. Genes Brain Behav. 14, 22–36 (2015).

    Article 
    PubMed 

    Google Scholar 

  • Beyeler, A. et al. Organization of valence-encoding and projection-defined neurons in the basolateral amygdala. Cell Rep. 22, 905–918 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, X. et al. Genetically identified amygdala–striatal circuits for valence-specific behaviors. Nat. Neurosci. 24, 1586–1600 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kyriazi, P., Headley, D. B. & Pare, D. Multi-dimensional coding by basolateral amygdala article multi-dimensional coding by basolateral amygdala neurons. Neuron 99, 1315–1328 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • O’Neill, P.-K. et al. The representational geometry of emotional states in basolateral amygdala. Preprint at bioRxiv https://doi.org/10.1101/2023.09.23.558668 (2023).

  • Headley, D. B., Kanta, V., Kyriazi, P. & Paré, D. Embracing complexity in defensive networks. Neuron 103, 189–201 (2019).

  • Ehret, B. et al. Population-level coding of avoidance learning in medial prefrontal cortex. Nat. Neurosci. 27, 1805–1815 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hyman, J. M., Whitman, J., Emberly, E., Woodward, T. S. & Seamans, J. K. Action and outcome activity state patterns in the anterior cingulate cortex. Cereb. Cortex 23, 1257–1268 (2013).

    Article 
    PubMed 

    Google Scholar 

  • Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Flavell, S. W., Gogolla, N., Lovett-Barron, M. & Zelikowsky, M. The emergence and influence of internal states. Neuron 110, 2545–2570 (2022).

  • Grillon, C. Associative learning deficits increase symptoms of anxiety in humans. Biol. Psychiatry 51, 851–858 (2002).

    Article 
    PubMed 

    Google Scholar 

  • Jo, Y. S., Heymann, G. & Zweifel, L. S. Dopamine neurons reflect the uncertainty in fear generalization. Neuron 100, 916–925 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Griffiths, K. R., Morris, R. W. & Balleine, B. W. Translational studies of goal-directed action as a framework for classifying deficits across psychiatric disorders. Front. Syst. Neurosci. 8, 101 (2014).

  • Bienvenu, T. C. M. et al. The advent of fear conditioning as an animal model of post-traumatic stress disorder: learning from the past to shape the future of PTSD research. Neuron 109, 2380–2397 (2021).

  • Sookman, D. & Pinard, G. in Cognitive Approaches to Obsessions and Compulsions (eds Frost, R. O. & Steketee, G.) Ch. 5 (Elsevier, 2002).

  • Peschard, V. & Philippot, P. Overestimation of threat from neutral faces and voices in social anxiety. J. Behav. Ther. Exp. Psychiatry 57, 206–211 (2017).

    Article 
    PubMed 

    Google Scholar 

  • Grupe, D. W. & Nitschke, J. B. Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective. Nat. Rev. Neurosci. 14, 488–501 (2013).

  • Blair, K. S. et al. Reduced optimism and a heightened neural response to everyday worries are specific to generalized anxiety disorder, and not seen in social anxiety. Psychol. Med. 47, 1806–1815 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Likhtik, E., Stujenske, J. M., Topiwala, M. A., Harris, A. Z. & Gordon, J. A. Prefrontal entrainment of amygdala activity signals safety in learned fear and innate anxiety. Nat. Neurosci. 17, 106–113 (2014).

    Article 
    PubMed 

    Google Scholar 

  • Felix-Ortiz, A. C. et al. The infralimbic and prelimbic cortical areas bidirectionally regulate safety learning during normal and stress conditions. Preprint at bioRxiv https://doi.org/10.1101/2023.05.05.539516 (2023).

  • Deserno, L., Boehme, R., Heinz, A. & Schlagenhauf, F. Reinforcement learning and dopamine in schizophrenia: dimensions of symptoms or specific features of a disease group? Front. Psychiatry 4, 172 (2013).

  • Jia, R. et al. Neural valuation of rewards and punishments in posttraumatic stress disorder: a computational approach. Transl. Psychiatry 13, 101 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates 2nd edn (Elsevier, 2001).

  • Capuzzo, G. & Floresco, S. B. Prelimbic and infralimbic prefrontal regulation of active and inhibitory avoidance and reward-seeking. J. Neurosci. 40, 4773–4787 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Hastie, T. Ridge regularization: an essential concept in data science. Technometrics 62, 426–433 (2020).

    Article 
    MathSciNet 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn, corrected 12th printing Jan 2017 (Springer, 2009).

  • Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. Advances in Neural Information Processing Systems 30 (eds Guyon, I. et al.) (NIPS, 2017).


  • ■ مصدر الخبر الأصلي

    نشر لأول مرة على: www.nature.com

    تاريخ النشر: 2026-01-07 02:00:00

    الكاتب: Nanci Winke

    تنويه من موقع “yalebnan.org”:

    تم جلب هذا المحتوى بشكل آلي من المصدر:
    www.nature.com
    بتاريخ: 2026-01-07 02:00:00.
    الآراء والمعلومات الواردة في هذا المقال لا تعبر بالضرورة عن رأي موقع “yalebnan.org”، والمسؤولية الكاملة تقع على عاتق المصدر الأصلي.

    ملاحظة: قد يتم استخدام الترجمة الآلية في بعض الأحيان لتوفير هذا المحتوى.

    yalebnan.org — متابعة الخبر