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

INCF Lithuanian Workshop on Neuroscience and Information Technology

The aim of this workshop was to give a current overview of neuroscience and informatics research in Lithuania, and to discuss the strategies for forming the Lithuanian Neuroinformatics Node and becoming a member of INCF. The workshop was organized by Dr. Aušra Saudargiene (Department of Informatics, Vytautas Magnus University, Kaunas, and Faculty of Natural Sciences, Vilnius University, Lithuania) and INCF.
The workshop was attended by 15 invited speakers, among them 4 guests and 11 Lithuanian neuroscientists, and over 20 participants. The workshop was organized into three main sessions: overview of the INCF activities including the Swedish and UK nodes of INCF; presentations on Neuroscience research carried out in Lithuania; discussion about the strategies for forming an INCF national node, and the benefits of having such a node in Lithuania (Appendix A: Program; Appendix B: Abstracts)

A reinforcement learning model of reaching integrating kinematic and dynamic control in a simulated arm robot

Models proposed within the literature of motor control have polarised around two classes of controllers which differ in terms of controlled variables: the Force-Control Models(FCMs), based on dynamic control, and the Equilibrium-Point Models (EPMs), based on kinematic control. This paper proposes a bioinspired model which aims to exploit the strengths of the two classes of models. The model is tested with a 3D physical simulator of a 2DOF-controlled arm robot engaged in a reaching task which requires the production of curved trajectories to be solved. The model is based on an actor-critic reinforcementlearning algorithm which uses neural maps to represent both percepts and actions encoded as joint-angle desired equilibrium points (EPs), and a noise generator suitable for fine tuning the exploration/exploitation ratio. The tests of the model show how it is capable of exploiting the simplicity and speed of learning of EPMs as well as the flexibility of FCMs in generating curved trajectories. Overall, the model represents a first step towards the generation of models which exploit the strengths of both EPMs and FCMs and has the potential of being used as a new tool for investigating phenomena related to the organisation and learning of motor behaviour in organisms

Closing the loop between neural network simulators and the OpenAI Gym

Since the enormous breakthroughs in machine learning over the last decade, functional neural network models are of growing interest for many researchers in the field of computational neuroscience. One major branch of research is concerned with biologically plausible implementations of reinforcement learning, with a variety of different models developed over the recent years. However, most studies in this area are conducted with custom simulation scripts and manually implemented tasks. This makes it hard for other researchers to reproduce and build upon previous work and nearly impossible to compare the performance of different learning architectures. In this work, we present a novel approach to solve this problem, connecting benchmark tools from the field of machine learning and state-of-the-art neural network simulators from computational neuroscience. This toolchain enables researchers in both fields to make use of well-tested high-performance simulation software supporting biologically plausible neuron, synapse and network models and allows them to evaluate and compare their approach on the basis of standardized environments of varying complexity. We demonstrate the functionality of the toolchain by implementing a neuronal actor-critic architecture for reinforcement learning in the NEST simulator and successfully training it on two different environments from the OpenAI Gym

Working memory and working attention: What could possibly evolve?

The concept of “working” memory is traceable back to nineteenth century theorists (Baldwin, 1894; James 1890) but the term itself was not used until the mid-twentieth century (Miller, Galanter & Pribram, 1960). A variety of different explanatory constructs have since evolved which all make use of the working memory label (Miyake & Shah, 1999). This history is briefly reviewed and alternative formulations of working memory (as language-processor, executive attention, and global workspace) are considered as potential mechanisms for cognitive change within and between individuals and between species. A means, derived from the literature on human problem-solving (Newell & Simon, 1972), of tracing memory and computational demands across a single task is described and applied to two specific examples of tool-use by chimpanzees and early hominids. The examples show how specific proposals for necessary and/or sufficient computational and memory requirements can be more rigorously assessed on a task by task basis. General difficulties in connecting cognitive theories (arising from the observed capabilities of individuals deprived of material support) with archaeological data (primarily remnants of material culture) are discussed

Neuro-Anatomical Changes of Carbon Monoxide Poisoning on Advanced Imaging: A Literature Review

Carbon monoxide (CO) poisoning is a major public health issue in the United States that accounts for approximately 50% of poisoning cases in the nation each year and around 50,000 emergency room visits. In most instances of CO poisoning, the culprit is a malfunctioning or poorly tended heating system within the home or, occasionally, commercial building, which causes the system to leak this hazardous gas. One of the more insidious aspects of CO poisoning is that the gas is odorless and colorless, and victims of CO poisoning often do not realize that there is a problem until they begin to experience the effects of poisoning and have no choice but to seek medical attention. Unfortunately, many victims of CO poisoning die before they are able to seek treatment. This paper makes use of a qualitative, systematic literature review to examine the four major parts of the brain that are most severely affected by CO poisoning. Overall, the literature review showed that the white matter, globus pallidus, basal ganglia, and cortex are the parts of the brain most severely impacted by CO poisoning. While many CO poisoning victims do make it to the hospital on time and are treated, they may nonetheless suffer long-term neurological consequences as a result of their exposure. As such, CO poisoning is a major public health issue