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

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889192397 Year: Pages: 123 DOI: 10.3389/978-2-88919-239-7 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2015-11-16 15:44:59
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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.

Neural information processing with dynamical synapses

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889193837 Year: Pages: 178 DOI: 10.3389/978-2-88919-383-7 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2015-12-03 13:02:24
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Experimental data have consistently revealed that the neuronal connection weight, which models the efficacy of the firing of a pre-synaptic neuron in modulating the state of a post-synaptic one, varies on short time scales, ranging from hundreds to thousands of milliseconds. This is called short-term plasticity (STP). Two types of STP, with opposite effects on the connection efficacy, have been observed in experiments. They are short-term depression (STD) and short-term facilitation (STF). Computational studies have explored the impact of STP on network dynamics, and found that STP can generate very rich intrinsic dynamical behaviors, including damped oscillations, state hopping with transient population spikes, traveling fronts and pulses, spiral waves, rotating bump states, robust self-organized critical activities and so on. These studies also strongly suggest that STP can play many important roles in neural computation. For instances, STD may provide a dynamic control mechanism that allows equal fractional changes on rapidly and slowly firing afferents to produce post-synaptic responses, realizing Weber's law; STD may provide a mechanism to close down network activity naturally, achieving iconic sensory memory; and STF may provide a mechanism for implementing work-memory not relying on persistent neural firing. From the computational point of view, the time scale of STP resides between fast neural signaling (in the order of milliseconds) and rapid learning (in the order of minutes or above), which is the time scale of many important temporal processes occurring in our daily lives, such as motion control and working memory. Thus, STP may serve as a substrate for neural systems manipulating temporal information on the relevant time scales. This Research Topic aims to present the recent progress in understanding the roles of STP in neural information processing. It includes, but no exclusively, the studies on investigating various computational roles of STP, the modeling studies on exploring new dynamical behaviors generated by STP, and the experimental works which help us to understand the functional roles of STP.

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2014 (2)