Improved representations and hardware implementation of the Helmholtz Machine

Probabilistic computing forms a relatively new computational style, of significant practical interest because stochastic behaviour is common and must be taken into accountin in biological and other real-world processes. We examine a particular stochastic ANN architecture, the Helmholtz Machine, investigating its characteristics, with particular respect to its wake-sleep training algorithm, and showing how its representational power might be increased. We also explain how we are implementing a simple version of the machine in analogue VLSI hardware

Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes

Introduction. Development of a robotic arm that can be operated using an exoskeletal position sensing harness as well as a dry electrode brain-computer interface headset. Design priorities comprise an intuitive and immersive user interface, fast and smooth movement, portability, and cost minimization. Materials and Methods. A robotic arm prototype capable of moving along 6 degrees of freedom has been developed, along with an exoskeletal position sensing harness which was used to control it. Commercially available dry electrode BCI headsets were evaluated. A particular headset model has been selected and is currently being integrated into the hybrid system. Results and Discussion. The combined arm-harness system has been successfully tested and met its design targets for speed, smooth movement, and immersive control. Initial tests verify that an operator using the system can perform pick and place tasks following a rather short learning curve. Further evaluation experiments are planned for the integrated BCI-harness hybrid setup. Conclusions. It is possible to design a portable robotic arm interface comparable in size, dexterity, speed, and fluidity to the human arm at relatively low cost. The combined system achieved its design goals for intuitive and immersive robotic control and is currently being further developed into a hybrid BCI system for comparative experiments

Detection of Eye Movements based on EEG Signals and the SAX algorithm

[[abstract]]For patients with disabilities, particularly those with motor disabilities and difficulties to interact with computer and devices, Human-Machine Interaction (HMI) research may provide them new ways to solve this problem. In this paper, we propose the Brain-Computer Interface (BCI) approach as a potential technique. The patients may use a portable electroencephalography (EEG) device to give instruction to a computing device via eye movements. Classification algorithms have been investigated in past research to allow detection of eye movement. We would like to investigate another technique, namely the Symbolic Aggregate Approximation (SAX) algorithm, to find out its suitability and performance against known classification algorithms such as Support Vector Machine (SVM), k-Nearest Neighbour (KNN) and Decision Tree (DT).[[notice]]補正完

Pulse-stream binary stochastic hardware for neural computation: the Helmholtz Machine

This thesis presents a novel hardware implementation of a binary-state, probabilistic artificial neuron using the pulse-stream analogue integrated circuit design methodology. The artificial neural network architecture targeted for implementation is the Helmholtz Machine, an auto-encoder trained by the unsupervised Wake-Sleep algorithm. A dual-layer network was implemented on one of two integrated circuit prototypes, intended for hardware-software comparative experiments in unsupervised probabilistic neural computation. Circuit modules were designed to perform the synaptic multiplication and integration functions, the sigmoid activation function, and to generate probabilistic output. All circuit design was modular and scaleable, with particular attention given to silicon area utilization and power consumption. The neuron outputs the calculated probability as a mark-to-period modulated stream of pulses, which is then randomly sampled to determine the next state for the neuron. Implementation issues are discussed, such as a tendency for the probabilistic oscillators inside each neuron to phase-lock or become unstable at highe

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals—namely, family members and professional carers—to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solution