Stochastic synaptic plasticity underlying compulsion in a model of addiction

Pascoli, Vincent; Hiver, Agnès; Zessen, Ruud van; Loureiro, Michaël; Achargui, Ridouane; Harada, Masaya; Flakowski, Jérôme; Lüscher, Christian

doi:10.1038/s41586-018-0789-4

Cited by 158 publications

(164 citation statements)

References 48 publications

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“…However, in this study, no distinct subpopulations of rats emerged, and the ratio of addicted animals remains to be determined. By contrast, using the same seek-take chain paradigm, we found a similar ratio of persevering mice that the one obtained when the taking lever was punished (Pascoli et al, 2018).…”

Section: Discussionsupporting

confidence: 62%

“…Compulsive SA may also reflect a deficit of inhibitory control, where drug seeking despite punishment persists due to the hypofunction of mPFC (Chen et al, 2013). By contrast, we have found a hyperactivity of the OFC to DS projection in mice complusively self-stimulating VTA DA neurons (Pascoli et al, 2018;, suggesting a decision making bias favoring reward pursuit despite the risk of punishment.…”

Section: Introductioncontrasting

confidence: 55%

“…Taken-together these data demonstrate that synaptic strength was increased in compulsive mice at OFC-cDS but not at mPFC-mDS or M1-lDS. These results suggest that compulsive oDASS seeking tested in a seek-take chain schedule could be mediated by OFC-cDS plasticity as it was shown in a version of the oDASS in which the active lever was punished (Pascoli et al, 2018).…”

Section: Transmission At Cortico-striatal Synapses In Compulsive Micementioning

confidence: 55%

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Cortico-striatal activity driving compulsive reward seeking

Harada

Pascoli

Hiver

et al. 2019

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Loss of control over drug intake and persistent drug-seeking despite negative consequences define addiction. Increase dopamine levels in the mesolimbic system may constitute the initial trigger. Optogenetic self-stimulation of VTA DA neurons (oDASS) has thus been proposed as an addiction model. Indeed, lever pressing to turn on a laser aimed at ChR2 expressing DA neurons is strongly reinforcing. Clinical observations indicate that drug-seeking even with the risk of harmful consequences occurs only in a fraction of users, with chronic drug consumption. Here, mice carried out a seek-take chain in order to selectively study compulsive seeking behavior. Once fully established, a probabilistic punishment of the seeking lever led to the emergence of two classes of mice; those that persevered and those that renounced oDASS. Ex vivo characterization of three distinct cortico-striatal streams demonstrated a selective potentiation of excitatory synapses of the orbito-frontal cortex (OFC) to dorsal striatum projection in persevering mice. Taken together, our data indicate a gain-of-function of OFC striatal control in compulsive oDASS. Harada et al. Compulsive oDASS seeking

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Section: Discussionsupporting

confidence: 62%

Section: Introductioncontrasting

confidence: 55%

Section: Transmission At Cortico-striatal Synapses In Compulsive Micementioning

confidence: 55%

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Cortico-striatal activity driving compulsive reward seeking

Harada

Pascoli

Hiver

et al. 2019

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“…revealed that the OFC is necessary for using expectations of specific outcome to guide behavior and learning 3,59 . The outcome predictions signaled by the OFC neurons could be utilized by downstream regions such as ventral striatum 60,61 , dorsal striatum 60,62 , BLA 63 and VTA 23,29,64 . OFC lesion or inactivation disrupted the prediction errors signaled by dopaminergic neurons 29,64 and expected reward values by putative non-dopaminergic neurons 64 in VTA.…”

Section: Studies Using Outcome Devaluation and Pavlovian Over-expectamentioning

confidence: 99%

Orbitofrontal control of visual cortex gain promotes visual associative learning

Liu

Deng

Zhang

et al. 2019

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4Signaling of expected outcomes in the orbitofrontal cortex (OFC) is critical for 5 outcome-guided and learning behavior. The OFC projects to primary visual 6 cortex (V1), yet the function of this top-down projection is unclear. We found 7 that optogenetic activation of OFC projection to V1 reduced the amplitude of V1 8 visual responses via the recruitment of local somatostatin-expressing (SST) 9interneurons. Using mice performing a Go/No-Go visual task, we showed that 10 the OFC projection to V1 mediated the suppression of V1 responses to the 11 reward-irrelevant No-Go stimulus. Furthermore, the responses of V1-projecting 12 OFC neurons to No-Go stimulus were reduced when the mice's expectation was 13 incorrect. In addition, optogenetic inactivation of OFC projection to V1 14 impaired, whereas activation of SST interneurons in V1 improved the learning of 15 Go/No-Go visual task. Thus, OFC top-down projection to V1 is crucial to drive 16 visual associative learning by reducing the response gain of V1 neurons to non-17 relevant stimulus. 18 19The OFC is a critical brain region for using the information about expected 20 outcomes to guide learning and behavior 1-3 . Studies in rodents and monkeys have 21 demonstrated that the identity and expected values of specific outcomes are 22 represented by activities in the OFC 4-14 . Lesions or inactivation of the OFC impair 23 behavior guided by outcome expectancy and learning driven by the discrepancy 24 48 V1 visual response by recruiting SST interneurons. We thus hypothesized that OFC 49 projection to V1 may filter out non-relevant visual information, and tested this 50 hypothesis in mice performing a Go/No-Go visual task. We found that the OFC 51 projection to V1 contributed to the suppression of V1 responses to the No-Go 52 stimulus, which was not associated with reward. Optogenetic tagging of V1-53 projecting OFC neurons revealed that their responses to the No-Go stimulus were 54 reduced in wrong trials but not in correct trials. We further showed that optogenetic 55 inactivation of OFC projection to V1 slowed the learning of Go/No-Go visual 56 behavior. Thus, the OFC projection to V1 plays a key role in filtering out non-relevant 57 visual information to facilitate associative learning. 58 59Results 60 OFC top-down projection controls V1 response amplitude by activation of local 61SST interneurons. We injected Cholera toxin subunit B (CTB) in V1 and found that 62 the retrograde labeled neurons were in the ventrolateral OFC (vlOFC) (Fig. 1a), 63 consistent with the finding in a previous study 38 . By injecting rAAV2-retro-hSyn-Cre 64 in V1 and AAV-DIO-EYFP in the OFC, we found that the axons of OFC neurons 65 terminated in both superficial and deep layers of V1 (Fig. 1b). To examine how the 66 5 OFC top-down projection influences V1 neuronal responses, we expressed excitatory 67 opsin Channelrhodopsin-2 (ChR2) or ChrimsonR in the OFC, and measured V1 68 responses with and without laser stimulation of OFC axons in mice passively viewing 69 drifting gratings (Fig. 1c)....

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“…Besides neurons, synapses are also stochastic in cerebral cortical [8]. Formation, elimination and volume change of dendritic spines exhibit random fluctuation [9][10][11][12][13][14][15] and transmitter release from synapses is also inherently unreliable [16][17][18]. These stochastic features may realize nearly optimal computation and learning under uncertain environment via Bayesian inference [19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Dual stochasticity in the cortex as a biologically plausible learning with the most efficient coding

Teramae

2019

Preprint

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Neurons and synapses in the cerebral cortex behave stochastically even during precise perception and reliable learning of animals. While studies have revealed advantages of these stochastic features, relationship and synergy of them remain elusive. Here, we show that these stochastic features are unified into a framework to provide an efficient and biologically-plausible learning algorithm that consistently explains various experimental findings of the brain, which includes statistics of cortical circuit and the most efficient power-low encoding of cortex. The algorithm can be regarded as an integration of major branches of existing algorithms of neural networks, back propagation learning and Boltzmann machine, while it overcomes known limitations of them. As an experimentally testable prediction, slow retrograde modulation of excitability of neurons is also derived. We anticipate that the algorithm is, due to its simplicity and flexibility, beneficial to develop neuromorphic devices or scalable AI-chips, and bridges the gap between neuroscience and machine learning.

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Stochastic synaptic plasticity underlying compulsion in a model of addiction

Cited by 158 publications

References 48 publications

Cortico-striatal activity driving compulsive reward seeking

Cortico-striatal activity driving compulsive reward seeking

Orbitofrontal control of visual cortex gain promotes visual associative learning

Dual stochasticity in the cortex as a biologically plausible learning with the most efficient coding

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