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 be...
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| Format: | Online |
| Język: | angielski |
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Frontiers Media SA
2021
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| Dostęp online: | 17651 |
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| _version_ | 1869518153993682944 |
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| author | Cornelius Glackin Julie Wall |
| author_browse | Cornelius Glackin Julie Wall |
| author_facet | Cornelius Glackin Julie Wall |
| author_sort | Cornelius Glackin |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-59844 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media SA |
| publisherStr | Frontiers Media SA |
| record_format | ojs |
| spelling | doab-20.500.12854ir-598442024-04-05T12:35:51Z Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding Cornelius Glackin Julie Wall RC321-571 Q1-390 Learning cell assembly sensory processing spike timing connectivity biological neurons Spiking Neural network thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences 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. 2021-02-12T04:16:56Z 2021-02-12T04:16:56Z 2015-11-16 15:44:59 2014 book 17651 16648714 9782889192397 https://directory.doabooks.org/handle/20.500.12854/59844 eng Frontiers Research Topics image/jpeg Attribution 4.0 International http://www.frontiersin.org/books/Spiking_Neural_Network_Connectivity_and_its_Potential_for_Temporal_Sensory_Processing_and_Variable_/281#nogo http://journal.frontiersin.org/researchtopic/1072/spiking-neural-network-connectivity-and-its-potential-for-temporal-sensory-processing-and-variable-b Frontiers Media SA 10.3389/978-2-88919-239-7 10.3389/978-2-88919-239-7 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889192397 123 open access |
| spellingShingle | RC321-571 Q1-390 Learning cell assembly sensory processing spike timing connectivity biological neurons Spiking Neural network thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences Cornelius Glackin Julie Wall Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding |
| title | Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding |
| title_full | Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding |
| title_fullStr | Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding |
| title_full_unstemmed | Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding |
| title_short | Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding |
| title_sort | spiking neural network connectivity and its potential for temporal sensory processing and variable binding |
| topic | RC321-571 Q1-390 Learning cell assembly sensory processing spike timing connectivity biological neurons Spiking Neural network thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| topic_facet | RC321-571 Q1-390 Learning cell assembly sensory processing spike timing connectivity biological neurons Spiking Neural network thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| url | 17651 |
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