The role of δ-opioid receptors in learning and memory underlying the development of addiction

Opioids are important endogenous ligands that exist in both invertebrates and vertebrates and signal by activation of opioid receptors to produce analgesia and reward or pleasure. The μ-opioid receptor is the best known of the opioid receptors and mediates the acute analgesic effects of opiates, while the δ-opioid receptor (DOR) has been less well studied and has been linked to effects that follow from chronic use of opiates such as stress, inflammation and anxiety. Recently, DORs have been shown to play an essential role in emotions and increasing evidence points to a role in learning actions and outcomes. The process of learning and memory in addiction has been proposed to involve strengthening of specific brain circuits when a drug is paired with a context or environment. The DOR is highly expressed in the hippocampus, amygdala, striatum and other basal ganglia structures known to participate in learning and memory. In this review, we will focus on the role of the DOR and its potential role in learning and memory underlying the development of addiction.

Orexin/hypocretin role in reward: implications for opioid and other addictions

Addiction is a devastating disorder that affects 15.3 million people worldwide. While prevalent, few effective treatments exist. Orexin receptors have been proposed as a potential target for anti-craving medications. Orexins, also known as hypocretins, are neuropeptides produced in neurons of the lateral and dorsomedial hypothalamus and perifornical area, which project widely throughout the brain. The absence of orexins in rodents and humans leads to narcolepsy. However, orexins also have an established role in reward seeking. This review will discuss some of the original studies describing the roles of the orexins in reward seeking as well as specific works that were presented at the 2013 International Narcotics Research Conference. Orexin signalling can promote drug-induced plasticity of glutamatergic synapses onto dopamine neurons of the ventral tegmental area (VTA), a brain region implicated in motivated behaviour. Additional evidence suggests that orexin signalling can also promote drug seeking by initiating an endocannabinoid-mediated synaptic depression of GABAergic inputs to the VTA, and thereby disinhibiting dopaminergic neurons. Orexin neurons co-express the inhibitory opioid peptide dynorphin. It has been proposed that orexin in the VTA may not mediate reward per se, but rather occludes the ‘anti-reward’ effects of dynorphin. Finally, orexin signalling in the prefrontal cortex and the central amygdala is implicated in reinstatement of reward seeking. This review will highlight recent work describing the role of orexin signalling in cellular processes underlying addiction-related behaviours and propose novel hypotheses for the mechanisms by which orexin signalling may impart drug seeking.

Correcting flaws in Mitchell’s analysis of EPBC

Efficient error-Propagating Block Chaining (EPBC) is a block cipher mode intended to simultaneously provide both confidentiality and integrity protection for messages. Mitchell’s analysis pointed out a weakness in the EPBC integrity mechanism that can be used in a forgery attack. This paper identifies and corrects a flaw in Mitchell’s analysis of EPBC, and presents other attacks on the EPBC integrity mechanism.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Tracking control of a class of Hamiltonian mechanical systems with disturbances

This paper presents a trajectory-tracking control strategy for a class of mechanical systems in Hamiltonian form. The class is characterised by a simplectic interconnection arising from the use of generalised coordinates and full actuation. The tracking error dynamic is modelled as a port-Hamiltonian Systems (PHS). The control action is designed to take the error dynamics into a desired closed-loop PHS characterised by a constant mass matrix and a potential energy with a minimum at the origin. A transformation of the momentum and a feedback control is exploited to obtain a constant generalised mass matrix in closed loop. The stability of the close-loop system is shown using the close-loop Hamiltonian as a Lyapunov function. The paper also considers the addition of integral action to design a robust controller that ensures tracking in spite of disturbances. As a case study, the proposed control design methodology is applied to a fully actuated robotic manipulator.

Efficient minimax strategies for square loss games

We consider online prediction problems where the loss between the prediction and the outcome is measured by the squared Euclidean distance and its generalization, the squared Mahalanobis distance. We derive the minimax solutions for the case where the prediction and action spaces are the simplex (this setup is sometimes called the Brier game) and the \ell_2 ball (this setup is related to Gaussian density estimation). We show that in both cases the value of each sub-game is a quadratic function of a simple statistic of the state, with coefficients that can be efficiently computed using an explicit recurrence relation. The resulting deterministic minimax strategy and randomized maximin strategy are linear functions of the statistic.