Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

Chronic symptoms after vestibular neuritis and the high velocity vestibulo-ocular reflex

Hypothesis: As the anterior and posterior semicircular canals are vital to the regulation of gaze stability, particularly during locomotion or vehicular travel, we tested whether the high velocity vestibulo‐ocular reflex (VOR) of the three ipsilesional semicircular canals elicited by the modified Head Impulse Test would correlate with subjective dizziness or vertigo scores after vestibular neuritis (VN). Background: Recovery following acute VN varies with around half reporting persistent symptoms long after the acute episode. However, an unanswered question is whether chronic symptoms are associated with impairment of the high velocity VOR of the anterior or posterior canals. Methods: Twenty patients who had experienced an acute episode of VN at least three months earlier were included in this study. Participants were assessed with the video head impulse test (vHIT) of all six canals, bithermal caloric irrigation, the Dizziness Handicap Inventory (DHI) and the Vertigo Symptoms Scale short‐form (VSS). Results: Of these 20 patients, 12 felt that they had recovered from the initial episode whereas 8 did not and reported elevated DHI and VSS scores. However, we found no correlation between DHI or VSS scores and the ipsilesional single or combined vHIT gain, vHIT gain asymmetry or caloric paresis. The high velocity VOR was not different between patients who felt they had recovered and patients who felt they had not. Conclusions: Our findings suggest that chronic symptoms of dizziness following VN are not associated with the high velocity VOR of the single or combined ipsilesional horizontal, anterior or posterior semicircular canals.