2008 Progress Report on Brain Research

Highlights new research on various disorders, nervous system injuries, neuroethics, neuroimmunology, pain, sense and body function, stem cells and neurogenesis, and thought and memory. Includes essays on arts and cognition and on deep brain stimulation

Neuro-informed Music Therapy for the Treatment of Anxiety and Depression: A Literature Review

This capstone thesis project is a literature review of research specifically regarding the neuroscience and neurochemistry of music and how it can inform music therapy treatment of mental health. Mental health is a large, overarching term that includes many disorders that refer to one’s psychological and/or emotional condition(s), which further includes an individual’s social well-being. This can include, but is not limited to, depression, anxiety, schizophrenia, and obsessive-compulsive disorder (OCD). This paper will focus in on a research-based, neuro-informed music therapy treatment of anxiety and depression. The goal of this paper was to provide research toward a future method in music therapy where therapists can take a research based and neuro-informed approach to treating anxiety and depression. With the current available research, it can be suggested that a neuro-informed music therapy approach can be used to treat mood disorders, specifically anxiety and depression, however, further research will be needed to support this method

Fault Recovery in Swarm Robotics Systems using Learning Algorithms

When faults occur in swarm robotic systems they can have a detrimental effect on collective behaviours, to the point that failed individuals may jeopardise the swarm's ability to complete its task. Although fault tolerance is a desirable property of swarm robotic systems, fault recovery mechanisms have not yet been thoroughly explored. Individual robots may suffer a variety of faults, which will affect collective behaviours in different ways, therefore a recovery process is required that can cope with many different failure scenarios. In this thesis, we propose a novel approach for fault recovery in robot swarms that uses Reinforcement Learning and Self-Organising Maps to select the most appropriate recovery strategy for any given scenario. The learning process is evaluated in both centralised and distributed settings. Additionally, we experimentally evaluate the performance of this approach in comparison to random selection of fault recovery strategies, using simulated collective phototaxis, aggregation and foraging tasks as case studies. Our results show that this machine learning approach outperforms random selection, and allows swarm robotic systems to recover from faults that would otherwise prevent the swarm from completing its mission. This work builds upon existing research in fault detection and diagnosis in robot swarms, with the aim of creating a fully fault-tolerant swarm capable of long-term autonomy

Core interpersonal patterns in complex trauma and the process of change in psychodynamic therapy : a case comparison study

We performed a case comparison study to investigate the nature of interpersonal patterns in childhood trauma and the process of change therein. We analyzed three matching cases of childhood trauma that followed a psychodynamic treatment via a mixed-methods design. We found that (1) the core tendency to avoid negative reactions from others through passive behaviors emerged in all three cases, both in childhood and adulthood, (2) core interpersonal patterns transpired in the interaction between patient and therapist and thereby affected the therapeutic relationship, and (3) change ensued when a repetition of core interpersonal patterns was avoided and a new relational experience occurred. The accumulated findings across cases further resulted in several clinical implications and recommendations, such as the importance of the assessment of patients' (covert) conditions, responsiveness, supervision and facilitating patients' agency, and provided several avenues for further research

LeaF: A Learning-based Fault Diagnostic System for Multi-Robot Teams

The failure-prone complex operating environment of a standard multi-robot application dictates some amount of fault-tolerance to be incorporated into every system. In fact, the quality of the incorporated fault-tolerance has a direct impact on the overall performance of the system. Despite the extensive work being done in the field of multi-robot systems, there does not exist a general methodology for fault diagnosis and recovery. The objective of this research, in part, is to provide an adaptive approach that enables the robot team to autonomously detect and compensate for the wide variety of faults that could be experienced. The key feature of the developed approach is its ability to learn useful information from encountered faults, unique or otherwise, towards a more robust system. As part of this research, we analyzed an existing multi-agent architecture, CMM – Causal Model Method – as a fault diagnostic solution for a sample multi-robot application. Based on the analysis, we claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors. However, the analysis also showed that the CMM method in its current form is incomplete as a turn-key solution. Due to the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Therefore, based on these preliminary studies, we designed an alternate approach, called LeaF: Learning based Fault diagnostic architecture for multi-robot teams. LeaF is an adaptive method that uses its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. LeaF combines the initial fault model with a case-based learning algorithm, LID – Lazy Induction of Descriptions — to allow robot team members to diagnose faults and to automatically update their causal models. The modified LID algorithm uses structural similarity between fault characteristics as a means of classifying previously un-encountered faults. Furthermore, the use of learning allows the system to identify and categorize unexpected faults, enable team members to learn from problems encountered by others, and make intelligent decisions regarding the environment. To evaluate LeaF, we implemented it in two challenging and dynamic physical multi-robot applications. The other significant contribution of the research is the development of metrics to measure the fault-tolerance, within the context of system performance, for a multi-robot system. In addition to developing these metrics, we also outline potential methods to better interpret the obtained measures towards truly understanding the capabilities of the implemented system. The developed metrics are designed to be application independent and can be used to evaluate and/or compare different fault-tolerance architectures like CMM and LeaF. To the best of our knowledge, this approach is the only one that attempts to capture the effect of intelligence, reasoning, or learning on the effective fault-tolerance of the system, rather than relying purely on traditional redundancy based measures. Finally, we show the utility of the designed metrics by applying them to the obtained physical robot experiments, measuring the effective fault-tolerance and system performance, and subsequently analyzing the calculated measures to help better understand the capabilities of LeaF

Towards Trouble-Free Networks for End Users

Network applications and Internet services fail all too frequently. However, end users cannot effectively identify the root cause using traditional troubleshooting techniques due to the limited capability to distinguish failures caused by local network elements from failures caused by elements located outside the local area network. To overcome these limitations, we propose a new approach, one that leverages collaboration of user machines to assist end users in diagnosing various failures related to Internet connectivity and poor network performance. First, we present DYSWIS ("Do You See What I See?"), an automatic network fault detection and diagnosis system for end users. DYSWIS identifies the root cause(s) of network faults using diagnostic rules that consider diverse information from multiple nodes. In addition, the DYSWIS rule system is specially designed to support crowdsourced and distributed probes. We also describe the architecture of DYSWIS and compare its performance with other tools. Finally, we demonstrate that the system successfully detects and diagnoses network failures which are difficult to diagnose using a single-user probe. Failures in lower layers of the protocol stack also have the potential to disrupt Internet access; for example, slow Internet connectivity is often caused by poor Wi-Fi performance. Channel contention and non-Wi-Fi interference are the primary reasons for this performance degradation. We investigate the characteristics of non-Wi-Fi interference that can severely degrade Wi-Fi performance and present WiSlow ("Why is my Wi-Fi slow?"), a software tool that diagnoses the root causes of poor Wi-Fi performance. WiSlow employs user-level network probes and leverages peer collaboration to identify the physical location of these causes. The software includes two principal methods: packet loss analysis and 802.11 ACK number analysis. When the issue is located near Wi-Fi devices, the accuracy of WiSlow exceeds 90%. Finally, we expand our collaborative approach to the Internet of Things (IoT) and propose a platform for network-troubleshooting on home devices. This platform takes advantage of built-in technology common to modern devices --- multiple communication interfaces. For example, when a home device has a problem with an interface it sends a probe request to other devices using an alternative interface. The system then exploits cooperation of both internal devices and remote machines. We show that this approach is useful in home networks by demonstrating an application that contains actual diagnostic algorithms

Evidence-based Nursing in the IED: From Caring to Curing?

Danish hospitals are major sites of healthcare reform, and new public management accountability and performance management tools have been applied to improve the quality and efficiency of services. One consequence of this is that nurses’ work in hospitals is increasingly standardized through medical evidence. Using Bourdieu’s theory of practice in combination with an ethnographic field study, it is analyzed how the nurses of a Danish Integrated Emergency Department respond to the changing conditions of work. It is illuminated how two opposing approaches to nursing of humanistically and pluralistically oriented caring, and evidence-based scientifically oriented curing inform nursing in the department. The curing approach is however trumping the caring approach. Curing creates new nursing career pathways and is by some nurses embraced with enthusiasm. For others, the new situation creates tension and distress. It is illustrated how the nurses position their practice in relation to the changing working conditions taking sides for either curing or caring, or finding a way to maneuver in between the two. The article argues that the normative enforcement of the curing approach may carry unintended side effects to the goals of quality and efficiency enhancements

Evidence-based Nursing in the IED: From Caring to Curing?

Danish hospitals are major sites of healthcare reform, and new public management accountability and performance management tools have been applied to improve the quality and efficiency of services. One consequence of this is that nurses’ work in hospitals is increasingly standardized through medical evidence. Using Bourdieu’s theory of practice in combination with an ethnographic field study, it is analyzed how the nurses of a Danish Integrated Emergency Department respond to the changing conditions of work. It is illuminated how two opposing approaches to nursing of humanistically and pluralistically oriented caring, and evidence-based scientifically oriented curing inform nursing in the department. The curing approach is however trumping the caring approach. Curing creates new nursing career pathways and is by some nurses embraced with enthusiasm. For others, the new situation creates tension and distress. It is illustrated how the nurses position their practice in relation to the changing working conditions taking sides for either curing or caring, or finding a way to maneuver in between the two. The article argues that the normative enforcement of the curing approach may carry unintended side effects to the goals of quality and efficiency enhancements