Spiking neural network connectivity and its potential for temporal sensory processing and variable binding
Wall, J. and Glackin, Cornelius 2013. Spiking neural network connectivity and its potential for temporal sensory processing and variable binding. Frontiers in Computational Neuroscience. 7 (182), pp. 1-2.
|Authors||Wall, J. and Glackin, Cornelius|
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.
|Keywords||cell assembly; spiking neural network; spike timing; biological neurons; learning; connectivity; sensory processing|
|Journal||Frontiers in Computational Neuroscience|
|Journal citation||7 (182), pp. 1-2|
|Accepted author manuscript|
|Web address (URL)||http://dx.doi.org/10.3389/fncom.2013.00182|
|19 Dec 2013|
|Publication process dates|
|Deposited||21 Oct 2015|
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