The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents

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Novel approaches for the treatment of psychostimulant and opioid abuse-focus on opioid receptor-based therapies

INTRODUCTION: Psychostimulant and opioid addiction are poorly treated. The majority of abstinent users relapse back to drug-taking within a year of abstinence, making ‘anti-relapse’ therapies the focus of much current research. There are two fundamental challenges to developing novel treatments for drug addiction. Firstly, there are 3 key stimuli that precipitate relapse back to drug-taking: stress, presentation of drug-conditioned cue, taking a small dose of drug. The most successful novel treatment would be effective against all 3 stimuli. Secondly, a large number of drug users are poly-drug users: taking more than one drug of abuse at a time. The ideal anti-addiction treatment would therefore be effective against all classes of drugs of abuse. AREAS COVERED: In this review, the authors discuss the clinical need and animal models used to uncover potential novel treatments. There is a very broad range of potential treatment approaches and targets currently being examined as potential anti-relapse therapies. These broadly fit into 2 categories: ‘memory-based’ and ‘receptor-based’ and the authors discuss the key targets here within. EXPERT OPINION: Opioid receptors and ligands have been widely studied, and research into how different opioid subtypes affect behaviours related to addiction (reward, dysphoria, motivation) suggests that they are tractable targets as anti-relapse treatments. Regarding opioid ligands as novel ‘anti-relapse’ medications targets - research suggests that a ‘non-selective’ approach to targeting opioid receptors will be the most effective

The view from elsewhere: perspectives on ALife Modeling

Many artificial life researchers stress the interdisciplinary character of the field. Against such a backdrop, this report reviews and discusses artificial life, as it is depicted in, and as it interfaces with, adjacent disciplines (in particular, philosophy, biology, and linguistics), and in the light of a specific historical example of interdisciplinary research (namely cybernetics) with which artificial life shares many features. This report grew out of a workshop held at the Sixth European Conference on Artificial Life in Prague and features individual contributions from the workshop's eight speakers, plus a section designed to reflect the debates that took place during the workshop's discussion sessions. The major theme that emerged during these sessions was the identity and status of artificial life as a scientific endeavor

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Co-evolving Memetic Algorithms: Initial Investigations

This paper presents and examines the behaviour of a system whereby the rules governing local search within a Memetic Algorithm are co-evolved alongside the problem representation. We describe the rationale for such a system, and the implementation of a simple version in which the evolving rules are encoded as (condition:action) patterns applied to the problem representation, and are effectively self-adapted. We investigate the behaviour of the algorithm on a test suite of problems, and show significant performance improvements over a simple Genetic Algorithm, a Memetic Algorithm using a fixed neighbourhood function, and a similar Memetic Algorithm which uses random rules, i.e. with the learning mechanism disabled. Analysis of these results enables us to draw some conclusions about the way that even the simplified system is able to discover and exploit different forms of structure and regularities within the problems. We suggest that this “meta-learning” of problem features provides a means both of creating highly scaleable algorithms, and of capturing features of the solution space in an understandable form

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

Predictive validity of the UK clinical aptitude test in the final years of medical school:a prospective cohort study

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