Different propagation speeds of recalled sequences in plastic spiking neural networks
Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2015-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/17/3/035006 |
| _version_ | 1827873905083154432 |
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| author | Xuhui Huang Zhigang Zheng Gang Hu Si Wu Malte J Rasch |
| author_facet | Xuhui Huang Zhigang Zheng Gang Hu Si Wu Malte J Rasch |
| author_sort | Xuhui Huang |
| collection | DOAJ |
| description | Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments. |
| first_indexed | 2024-03-12T16:44:45Z |
| format | Article |
| id | doaj.art-a8bf67d5347d4b43b3a31896db41af97 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:44:45Z |
| publishDate | 2015-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-a8bf67d5347d4b43b3a31896db41af972023-08-08T14:19:01ZengIOP PublishingNew Journal of Physics1367-26302015-01-0117303500610.1088/1367-2630/17/3/035006Different propagation speeds of recalled sequences in plastic spiking neural networksXuhui Huang0Zhigang Zheng1Gang Hu2Si Wu3Malte J Rasch4Department of Physics , Beijing Normal University, People’s Republic of China; Brainnetome Center, Institute of Automation , Chinese Academy of Sciences, Beijing, People’s Republic of China; National Laboratory of Pattern Recognition , Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaDepartment of Physics , Beijing Normal University, People’s Republic of ChinaDepartment of Physics , Beijing Normal University, People’s Republic of ChinaState Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University , People’s Republic of China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University , People’s Republic of ChinaState Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University , People’s Republic of China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University , People’s Republic of ChinaNeural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.https://doi.org/10.1088/1367-2630/17/3/035006sequential activity recallspiking neural networkspike-timing dependent plasticity |
| spellingShingle | Xuhui Huang Zhigang Zheng Gang Hu Si Wu Malte J Rasch Different propagation speeds of recalled sequences in plastic spiking neural networks New Journal of Physics sequential activity recall spiking neural network spike-timing dependent plasticity |
| title | Different propagation speeds of recalled sequences in plastic spiking neural networks |
| title_full | Different propagation speeds of recalled sequences in plastic spiking neural networks |
| title_fullStr | Different propagation speeds of recalled sequences in plastic spiking neural networks |
| title_full_unstemmed | Different propagation speeds of recalled sequences in plastic spiking neural networks |
| title_short | Different propagation speeds of recalled sequences in plastic spiking neural networks |
| title_sort | different propagation speeds of recalled sequences in plastic spiking neural networks |
| topic | sequential activity recall spiking neural network spike-timing dependent plasticity |
| url | https://doi.org/10.1088/1367-2630/17/3/035006 |
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