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These experiments compared isolation-reared and socially-reared rats in two complementary paradigms for measuring responding to signals of reward, both undrugged and following either systemic or intraaccumbens d-amphetamine (AMPH). In experiment 1, locomotor activity conditioned to food presentation was measured in rats exposed to a restricted feeding schedule. The interaction between this conditioned activity, AMPH administration (0.5, 2.0, 3.5, 5.0 mg/kg IP) and motivational state was measured. In experiment 2, hungry rats were trained to associate a compound light/noise stimulus with sucrose reward and were then implanted with guide cannulae in the nucleus accumbens. In the test phase, responding on one of two novel levers produced the compound stimulus (conditioned reinforcer; CR). Responses on the other lever had no effect. Each rat received four counterbalanced intra-accumbens infusions of AMPH (0, 3, 10, 20 micrograms). In both experiments, isolated rats responded more with stimuli associated with reward and this differential rearing effect was further exaggerated by AMPH. The isolation-induced sensitivity to these stimuli and to AMPH was critically dependent on motivational variables. Thus, in experiment 1 there were no differences between the groups when sated or during extinction and in experiment 2 the increased responding was restricted to the lever providing CR. Measurements of the locomotor response to intra-accumbens AMPH (0, 3, 10 micrograms) also revealed that isolated rats were more sensitive to a low dose of the drug when tested food-deprived in a relatively novel environment. These results suggest that the experience of isolation-rearing interacts either directly or indirectly with dopamine-dependent mechanisms of the nucleus accumbens to enhance the effects of reward-related stimuli.

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We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.