Neuronal adaptation translates stimulus gaps into a population code.
Neurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neur...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0095705&type=printable |
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| _version_ | 1850026344978579456 |
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| author | Chun-Wei Yuan Leila Khouri Benedikt Grothe Christian Leibold |
| author_facet | Chun-Wei Yuan Leila Khouri Benedikt Grothe Christian Leibold |
| author_sort | Chun-Wei Yuan |
| collection | DOAJ |
| description | Neurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neurophysiological recordings and in a computational network model. Independent component analysis (ICA) of experimental data from the inferior colliculus of Mongolian gerbils reveals that the network encodes different gap sizes primarily with its population firing rate within 30 ms after the presentation of the gap, where longer gap size evokes higher network activity. We then developed a computational model to investigate possible mechanisms of how to generate the population code for gaps. Phenomenological (ICA) and functional (discrimination performance) analyses of our simulated networks show that the experimentally observed patterns may result from heterogeneous adaptation, where adaptation provides gap detection at the single neuron level and neuronal heterogeneity ensures discriminable population codes for the whole range of gap sizes in the input. Furthermore, our work suggests that network recurrence additionally enhances the network's ability to provide discriminable population patterns. |
| format | Article |
| id | doaj-art-b32aa7afe0c64ae8b7a297bfe06f4a14 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b32aa7afe0c64ae8b7a297bfe06f4a142025-08-20T03:00:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0194e9570510.1371/journal.pone.0095705Neuronal adaptation translates stimulus gaps into a population code.Chun-Wei YuanLeila KhouriBenedikt GrotheChristian LeiboldNeurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neurophysiological recordings and in a computational network model. Independent component analysis (ICA) of experimental data from the inferior colliculus of Mongolian gerbils reveals that the network encodes different gap sizes primarily with its population firing rate within 30 ms after the presentation of the gap, where longer gap size evokes higher network activity. We then developed a computational model to investigate possible mechanisms of how to generate the population code for gaps. Phenomenological (ICA) and functional (discrimination performance) analyses of our simulated networks show that the experimentally observed patterns may result from heterogeneous adaptation, where adaptation provides gap detection at the single neuron level and neuronal heterogeneity ensures discriminable population codes for the whole range of gap sizes in the input. Furthermore, our work suggests that network recurrence additionally enhances the network's ability to provide discriminable population patterns.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0095705&type=printable |
| spellingShingle | Chun-Wei Yuan Leila Khouri Benedikt Grothe Christian Leibold Neuronal adaptation translates stimulus gaps into a population code. PLoS ONE |
| title | Neuronal adaptation translates stimulus gaps into a population code. |
| title_full | Neuronal adaptation translates stimulus gaps into a population code. |
| title_fullStr | Neuronal adaptation translates stimulus gaps into a population code. |
| title_full_unstemmed | Neuronal adaptation translates stimulus gaps into a population code. |
| title_short | Neuronal adaptation translates stimulus gaps into a population code. |
| title_sort | neuronal adaptation translates stimulus gaps into a population code |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0095705&type=printable |
| work_keys_str_mv | AT chunweiyuan neuronaladaptationtranslatesstimulusgapsintoapopulationcode AT leilakhouri neuronaladaptationtranslatesstimulusgapsintoapopulationcode AT benediktgrothe neuronaladaptationtranslatesstimulusgapsintoapopulationcode AT christianleibold neuronaladaptationtranslatesstimulusgapsintoapopulationcode |