Neural information processing with dynamical synapses
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-ter...
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Frontiers Media SA
2021
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| author | Misha Tsodyks Si Wu Michael K Y Wong |
| author_browse | Michael K Y Wong Misha Tsodyks Si Wu |
| author_facet | Misha Tsodyks Si Wu Michael K Y Wong |
| author_sort | Misha Tsodyks |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-54475 |
| 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-544752024-04-05T17:30:25Z Neural information processing with dynamical synapses Misha Tsodyks Si Wu Michael K Y Wong RC321-571 Q1-390 neural field model Associative Memory neural information processing phenomenological model network dynamics short-term plasticity Continuous Attractor Neural Network thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences 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. 2021-02-11T20:48:02Z 2021-02-11T20:48:02Z 2015-12-03 13:02:24 2014 book 17753 16648714 9782889193837 https://directory.doabooks.org/handle/20.500.12854/54475 eng Frontiers Research Topics image/jpeg Attribution 4.0 International http://www.frontiersin.org/books/Neural_Information_Processing_with_Dynamical_Synapses/408#nogo http://journal.frontiersin.org/researchtopic/821/neural-information-processing-with-dynamical-synapses Frontiers Media SA 10.3389/978-2-88919-383-7 10.3389/978-2-88919-383-7 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889193837 178 open access |
| spellingShingle | RC321-571 Q1-390 neural field model Associative Memory neural information processing phenomenological model network dynamics short-term plasticity Continuous Attractor Neural Network thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences Misha Tsodyks Si Wu Michael K Y Wong Neural information processing with dynamical synapses |
| title | Neural information processing with dynamical synapses |
| title_full | Neural information processing with dynamical synapses |
| title_fullStr | Neural information processing with dynamical synapses |
| title_full_unstemmed | Neural information processing with dynamical synapses |
| title_short | Neural information processing with dynamical synapses |
| title_sort | neural information processing with dynamical synapses |
| topic | RC321-571 Q1-390 neural field model Associative Memory neural information processing phenomenological model network dynamics short-term plasticity Continuous Attractor 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 neural field model Associative Memory neural information processing phenomenological model network dynamics short-term plasticity Continuous Attractor Neural Network thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| url | 17753 |
| work_keys_str_mv | AT mishatsodyks neuralinformationprocessingwithdynamicalsynapses AT siwu neuralinformationprocessingwithdynamicalsynapses AT michaelkywong neuralinformationprocessingwithdynamicalsynapses |