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The geometri representation theory of these algebras has been studied in [Lu1, Lu2, Lu3 and fundamental re-sults have appeared in [HO, Op. ... The following proposition provides a graded He ke analogue of the results in [Ka,Proposition 1.20 and [Ka,

Science 2005, 307:1642-1645. 24. Zaghloul KA, Blanco JA, Weidemann CT, McGill K, Jaggi JL, Baltuch GH, Kahana MJ: Human substantia nigra neurons encode unexpected financial rewards.

p is a p-complete, nilpotent space and π1(BG. p )= G/Op(G). ... HBG = HBNG(D) = HB(NG(D)/Op′NG(D)) = HBDNG(D)/CG(D). Proof. See Swan [209].

dV (t) =. ((ıH 1. 2aKKa. )dt dB(t)Ka. aKSdB(t) Tr((S I)>dΛ(t)))V (t),. ... dY (t) = Ka(t)dt SdB(t). C Controllability and Observability. Consider a complex linear (time invariant state-space) system described by the differentialequation.

Using the isometry z and the above identification of spinc bundles any Dirac op-. ... lim inf|k|. 1. |k|#{λ spec(PD,kA) : λ ε2k. } 1. 2π.

NEURONAL REWARD AND DECISION SIGNALS:FROM THEORIES TO DATAWolfram Schultz. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UnitedKingdom. LSchultz W. Neuronal Reward and Decision Signals: From Theories to

these points. Once the sample size n is fixed, either by cost or time considerations, and an op-.

Neuronal reward and decision signals: from theories to data Wolfram Schultz Department of Physiology, Development and Neuroscience, University of Cambridge Cambridge CB2 3DY, United Kingdom ws234@cam.ac.uk Physiol Rev 95: 853-951, 2015 I.

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