Gerard David Howard

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The purpose of this investigation is to determine the normal values for regional cerebral blood flow (rCBF) as determined by the xenon inhalation method of Obrist. Normal values for all rCBF parameters were measured in 15 healthy individuals. Our data are compared with the normal data obtained by other investigators. In addition, test-retest rCBF(More)
We describe a new neuromuscular disorder in the kyphoscoliotic mouse mutant (ky). Mice were killed at ages from birth to 210 days, and tissues were taken for standard light microscopy, histochemistry, nerve ending studies, and electron microscopy. At birth a few myofibers showed phagocytosis ultrastructurally. Between 6 and 25 days there was prominent(More)
This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to emerge. By comparing(More)
For artificial entities to achieve high degrees of autonomy they will need to display appropriate adaptability. In this sense adaptability includes representational flexibility guided by the environment at any given time. This paper presents the use of constructivism-inspired mechanisms within a neural learning classifier system which exploits parameter(More)
This paper presents a Learning Classifier System (LCS) where each classifier condition is represented by a spiking neural network. Adaptive behavior is realized through the use of self-adaptive parameters and neural constructivism, providing the system with a flexible knowledge representation. The approach allows for the evolution of networks of appropriate(More)
For artificial entities to achieve high degrees of autonomy they will need to display appropriate adaptability. In this sense adaptability includes representational flexibility guided by the environment at any given time. This paper presents the use of constructivism-inspired mechanisms within a neural learning classifier system which exploits parameter(More)
Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and(More)
This paper presents a Learning Classifier System (LCS) where each classifier condition is represented by a feed-forward multi-layered perceptron (MLP) network. Adaptive behavior is realized through the use of self-adaptive parameters and neural constructivism, providing the system with a flexible knowledge representation. The approach allows for the(More)
This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and a constructionist approach, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to be(More)