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Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation plays a fundamental role in instrumental performance and associated mesolimbic dopamine signaling.

Author(s): Emanuela Santini [sup.1] , Thu N. Huynh [sup.1] , Andrew F. MacAskill [sup.1] , Adam G. Carter [sup.1] , Philippe Pierre [sup.2] [sup.3] [sup.4] , Davide Ruggero [sup.5] , [...], Mice overexpressing eIF4E show autism-related behaviours and altered synaptic activity in the hippocampus, prefrontal cortex and striatum, and these phenotypes can be rescued with the cap-dependent translation inhibitor 4EGI-1. Targets for autism spectrum disorder treatment Aberrant protein synthesis has been hypothesized as one causal mechanism of autism spectrum disorders (ASDs), but the details of which pathways are disrupted remain unknown. Disruption of eIF4E, a key factor for translation initiation, has been associated with human autism, and now two independent papers implicate excessive cap-dependent translation in synaptic and ASD-related behavioural deficits in mice. Nahum Sonenberg and colleagues show that mice lacking 4E-BP2, an eIF4E repressor, display increased translation of neuroligins, synaptic proteins strongly implicated in autism. The mice also display ASD-related behaviors and alterations in hippocampal synaptic activity, which are reversed by normalization of eIF4E activity or neuroligin 1 levels. Eric Klann and colleagues show that mice overexpressing eIF4E also display ASD-related behaviours and altered synaptic activity in the hippocampus, prefrontal cortex and striatum, and that some phenotypes can be rescued with the cap-dependent translation inhibitor 4EGI-1. The converging results from these two studies implicate cap-dependent translation as a potential therapeutic target for treatment of ASD-related symptoms. Autism spectrum disorders (ASDs) are an early onset, heterogeneous group of heritable neuropsychiatric disorders with symptoms that include deficits in social interaction skills, impaired communication abilities, and ritualistic-like repetitive behaviours.sup.1,2. One of the hypotheses for a common molecular mechanism underlying ASDs is altered translational control resulting in exaggerated protein synthesis.sup.3. Genetic variants in chromosome 4q, which contains the EIF4E locus, have been described in patients with autism.sup.4,5. Importantly, a rare single nucleotide polymorphism has been identified in autism that is associated with increased promoter activity in the EIF4E gene.sup.6. Here we show that genetically increasing the levels of eukaryotic translation initiation factor 4E (eIF4E) in mice.sup.7 results in exaggerated cap-dependent translation and aberrant behaviours reminiscent of autism, including repetitive and perseverative behaviours and social interaction deficits. Moreover, these autistic-like behaviours are accompanied by synaptic pathophysiology in the medial prefrontal cortex, striatum and hippocampus. The autistic-like behaviours displayed by the eIF4E-transgenic mice are corrected by intracerebroventricular infusions of the cap-dependent translation inhibitor 4EGI-1. Our findings demonstrate a causal relationship between exaggerated cap-dependent translation, synaptic dysfunction and aberrant behaviours associated with autism.

Modification of the number of GABAA receptors ([GABA.sub.A]Rs) clustered at inhibitory synapses can regulate inhibitory synapse strength with important implications for information processing and nervous system plasticity and pathology. Currently, however, the mechanisms that regulate the number of [GABA.sub.A]Rs at synapses remain poorly understood. By imaging superecliptic pHluorin tagged GABAAR subunits we show that synaptic [GABA.sub.A]R clusters are normally stable, but that increased neuronal activity upon glutamate receptor (GluR) activation results in their rapid and reversible dispersal. This dispersal correlates with increases in the mobility of single [GABA.sub.A]RS within the clusters as determined using single-particle tracking of [GABA.sub.A]Rs labeled with quantum dots. GluR-dependent dispersal of [GABA.sub.A]R clusters requires [Ca.sup.2+] influx via NMDA receptors (NMDARs) and activation of the phosphatase calcineurin. Moreover, the dispersal of [GABA.sub.A]R clusters and increased mobility of individual [GABA.sub.A]RS are dependent on serine 327 within the intracellular loop of the [GABA.sub.A]R [gamma]2 subunit. Thus, NMDAR signaling, via calcineurin and a key [GABA.sub.A]R phosphorylation site, controls the stability of synaptic [GABA.sub.A]Rs, with important implications for activity-dependent control of synaptic inhibition and neuronal plasticity. ion channels | plasticity | trafficking | diffusion | calcineurin doi/ 10.1073/pnas.1000589107

Modification of the number of GABAA receptors (GABAARS) clustered at inhibitory synapses can regulate inhibitory synapse strength with important implications for information processing and nervous system plasticity and pathology. Currently, however, the mechanisms that regulate the number of GABAARS at synapses remain poorly understood. By imaging superecliptic pHluonn tagged GABAAR subunits we show that synaptic GABAAR clusters are normally stable, but that increased neuronal activity upon glutamate receptor (GIuR) activation results in their rapid and reversible dispersal. This dispersal correlates with increases in the mobility of single GABAARs within the clusters as determined using singleparticle tracking of GABAARs labeled with quantum dots. GluRdependent dispersal of GABAAR clusters requires Ca2+ influx via NMDA receptors (NMDARs) and activation of the phosphatase calcineurin. Moreover, the dispersal of GABAAR clusters and increased mobility of individual GABAARs are dependent on serine 327 within the intracellular loop of the GABAAR γ2 subunit Thus, NMDAR signaling, via calcineunn and a key GABAAR phosphorylation site, controls the stability of synaptic GABAARS, with important implications for activity dependent control of synaptic inhibition and neuronal plasticity. [ABSTRACT FROM AUTHOR]

Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison among a wide set of cognitive models based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation contributes to instrumental task performance in mice and is reflected in mesolimbic dopamine signaling.