Resolution enhancement in neural networks with dynamical synapses
Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-06-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00073/full |
| _version_ | 1819259437693534208 |
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| author | C. C. Alan Fung He eWang Kin eLam K. Y. Michael Wong Si eWu |
| author_facet | C. C. Alan Fung He eWang Kin eLam K. Y. Michael Wong Si eWu |
| author_sort | C. C. Alan Fung |
| collection | DOAJ |
| description | Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general. |
| first_indexed | 2024-12-23T19:10:00Z |
| format | Article |
| id | doaj.art-2257771fae064d8e846ffd47857c9172 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-23T19:10:00Z |
| publishDate | 2013-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-2257771fae064d8e846ffd47857c91722022-12-21T17:34:30ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-06-01710.3389/fncom.2013.0007343569Resolution enhancement in neural networks with dynamical synapsesC. C. Alan Fung0He eWang1Kin eLam2K. Y. Michael Wong3Si eWu4The Hong Kong University of Science and TechnologyThe Hong Kong University of Science and TechnologyThe Hong Kong University of Science and TechnologyThe Hong Kong University of Science and TechnologyBeijing Normal UniversityConventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00073/fullneural field modelshort-term synaptic plasticityContinuous Attractor Neural Networkshort-term synaptic depressiontransparent motion |
| spellingShingle | C. C. Alan Fung He eWang Kin eLam K. Y. Michael Wong Si eWu Resolution enhancement in neural networks with dynamical synapses Frontiers in Computational Neuroscience neural field model short-term synaptic plasticity Continuous Attractor Neural Network short-term synaptic depression transparent motion |
| title | Resolution enhancement in neural networks with dynamical synapses |
| title_full | Resolution enhancement in neural networks with dynamical synapses |
| title_fullStr | Resolution enhancement in neural networks with dynamical synapses |
| title_full_unstemmed | Resolution enhancement in neural networks with dynamical synapses |
| title_short | Resolution enhancement in neural networks with dynamical synapses |
| title_sort | resolution enhancement in neural networks with dynamical synapses |
| topic | neural field model short-term synaptic plasticity Continuous Attractor Neural Network short-term synaptic depression transparent motion |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00073/full |
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