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. In recent years, much of the focus in neuron modeling 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 behavior of biological neurons. This research topic spans current research on neuron models to spiking neural networks and their application to interesting and current computational problems. The research papers submitted to this topic can be categorized into the following major areas of more efficient neuron modeling; lateral and recurrent spiking neural network connectivity; exploitation of biological neural circuitry by means of spiking neural networks; optimization of spiking neural networks; and spiking neural networks for sensory processing. Moujahid and d’Anjou (2012) stimulate the giant squid axon with simulated spikes to develop some new insights into the development of more relevant models of biological neurons. They observed that temperature mediates the efficiency of action potentials by reducing the overlap between sodium and potassium currents in the ion exchange and subsequent energy consumption. The original research article by Dockendorf and Srinivasa (2013) falls into the area of lateral and recurrent spiking neural network connectivity. It presents a recurrent spiking model capable of learning episodes featuring missing and noisy data. The presented topology provides a means of recalling previously encoded patterns where inhibition is of the high frequency variety aiming to promote stability of the network. Kaplan et al. (2013) also investigated the use of recurrent spiking connectivity in their work on motion-based prediction and the issue of missingdata. Heretheyaddresshowanisotropicconnectivity patterns that consider the tuning properties of neurons efficiently predict the trajectory of a disappearing moving stimulus. They demonstrate and test this by simulating the network response in a moving-dot blanking experiment.
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Jun 1, 2014
Jin-Ho Yeo + 3
Open Access
