Biomedical Engineering (BME) or Medical Engineering is the application of engineering principles and design concepts to medicine and biology for healthcare purposes. This field seeks to close the gap between engineering and medicine, combining the design and problem solving skills of engineering with medical biological sciences to advance health care treatment, including diagnosis, monitoring, and therapy. Also included under the scope of a biomedical engineer is the management of current medical equipment within hospitals while adhering to relevant industry standards. This involves equipment recommendations, procurement, routine testing and preventative maintenance, through to decommissioning and disposal. This role is also known as a Biomedical Equipment Technician (BMET) or clinical engineering.
Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge about cats, for example, that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the learning material that they process.
Computational neuroscience is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").
Bio-inspired computing, short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to machine learning. It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model the living phenomena, and simultaneously the study of life to improve the usage of computers. Biologically inspired computing is a major subset of natural computation.
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, topology and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution can be contrasted with conventional deep learning techniques that use gradient descent on a neural network with a fixed topology.
Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,, spintronic memories, threshold switches, and transistors.
Brain implants, often referred to as neural implants, are technological devices that connect directly to a biological subject's brain – usually placed on the surface of the brain, or attached to the brain's cortex. A common purpose of modern brain implants and the focus of much current research is establishing a biomedical prosthesis circumventing areas in the brain that have become dysfunctional after a stroke or other head injuries. This includes sensory substitution, e.g., in vision. Other brain implants are used in animal experiments simply to record brain activity for scientific reasons. Some brain implants involve creating interfaces between neural systems and computer chips. This work is part of a wider research field called brain-computer interfaces.
Neuroprosthetics is a discipline related to neuroscience and biomedical engineering concerned with developing neural prostheses. They are sometimes contrasted with a brain–computer interface, which connects the brain to a computer rather than a device meant to replace missing biological functionality.
Neural engineering is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties of neural systems. Neural engineers are uniquely qualified to solve design problems at the interface of living neural tissue and non-living constructs.
Neurohacking is the colloquial term for neuroengineering. It is a form of biohacking (qv) focusing on the brain and CNS. Strictly speaking it is any method of manipulating or interfering with the structure and/or function of neurons for improvement or repair.
Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models, and the other one searching for potential quantum effects in the brain.
John R. Hetling is an Associate Professor at the University of Illinois at Chicago in the Richard and Loan Hill Department of Bioengineering and Department of Ophthalmology and Visual Sciences. He is also the Director of Undergraduate Studies for the Department of Bioengineering and the Director of the Neural Engineering Vision Laboratory at UIC, and Chief Science Officer of RetMap, Inc. After developing ten different courses and teaching 13 different courses in Bioengineering at UIC, Dr. Hetling was recognized with the highest awards for teaching and education within UIC, and delivered keynote addresses and led workshops on bioengineering education and translational bioengineering, as well as many invited technical lectures delivered in five countries. At UIC, Hetling developed the first undergraduate course track in Neural Engineering, and in 2008 he and his students authored the most widely accepted definition of the field.
Spiking neural networks (SNNs) are artificial neural network models that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle, but rather fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal.
GENESIS is a simulation environment for constructing realistic models of neurobiological systems at many levels of scale including: sub-cellular processes, individual neurons, networks of neurons, and neuronal systems. These simulations are “computer-based implementations of models whose primary objective is to capture what is known of the anatomical structure and physiological characteristics of the neural system of interest”. GENESIS is intended to quantify the physical framework of the nervous system in a way that allows for easy understanding of the physical structure of the nerves in question. “At present only GENESIS allows parallelized modeling of single neurons and networks on multiple-instruction-multiple-data parallel computers.” Development of GENESIS software spread from its home at Caltech to labs at the University of Texas at San Antonio, the University of Antwerp, the National Centre for Biological Sciences in Bangalore, the University of Colorado, the Pittsburgh Supercomputing Center, the San Diego Supercomputer Center, and Emory University.
Bioelectronics is a field of research in the convergence of biology and electronics.
There are many types of artificial neural networks (ANN).
A hippocampus prosthesis is a type of cognitive prosthesis. Prosthetic devices replace normal function of a damaged body part; this can be simply a structural replacement or a rudimentary, functional replacement. However, prosthetics involving the brain have some special categories and requirements. "Input" prosthetics, such as retinal or cochlear implant, supply signals to the brain that the patient eventually learns to interpret as sight or sound. "Output" prosthetics use brain signals to drive a bionic arm, hand or computer device, and require considerable training during which the patient learns to generate the desired action via their thoughts. Both of these types of prosthetics rely on the plasticity of the brain to adapt to the requirement of the prosthesis, thus allowing the user to "learn" the use of his new body part. A cognitive or "brain-to-brain" prosthesis involves neither learned input nor output signals, but the native signals used normally by the area of the brain to be replaced. Thus, such a device must be able to fully replace the function of a small section of the nervous system—using that section's normal mode of operation. In order to achieve this, developers require a deep understanding of the functioning of the nervous system. The scope of design must include a reliable mathematical model as well as the technology in order to properly manufacture and install a cognitive prosthesis. The primary goal of an artificial hippocampus is to provide a cure for Alzheimer's disease and other hippocampus—related problems. To do so, the prosthesis has to be able to receive information directly from the brain, analyze the information and give an appropriate output to the cerebral cortex; in other words, it must behave just like a natural hippocampus. At the same time, the artificial organ must be completely autonomous, since any exterior power source will greatly increase the risk of infection.
Robin Angus Silver is Professor of Neuroscience and a Wellcome Trust Principal Research Fellow at University College London. His laboratory studies neurotransmission and artificial neural networks by combining in vitro and in vivo experimental approaches with quantitative analysis and computational models developed in silico.
