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Different propagation speeds of recalled sequences in plastic spiking neural networks
Huang, Xuhui1,2,3; Zheng, Zhigang1; Hu, Gang1; Wu, Si4,5,6; Rasch, Malte J.4,5,6; Si Wu, Malte J. Rasch
AbstractNeural 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.
KeywordSequential Activity Recall Spiking Neural Network Spike-timing Dependent Plasticity
WOS HeadingsScience & Technology ; Physical Sciences
Indexed BySCI
WOS Research AreaPhysics
WOS SubjectPhysics, Multidisciplinary
WOS IDWOS:000352899100005
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorSi Wu, Malte J. Rasch
Affiliation1.Beijing Normal Univ, Dept Phys, Beijing 100875, Peoples R China
2.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China
3.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
4.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
5.Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing 100875, Peoples R China
6.Beijing Normal Univ, Ctr Collaborat & Innovat Brain & Learning Sci, Beijing 100875, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Huang, Xuhui,Zheng, Zhigang,Hu, Gang,et al. Different propagation speeds of recalled sequences in plastic spiking neural networks[J]. NEW JOURNAL OF PHYSICS,2015,17(3):035006.
APA Huang, Xuhui,Zheng, Zhigang,Hu, Gang,Wu, Si,Rasch, Malte J.,&Si Wu, Malte J. Rasch.(2015).Different propagation speeds of recalled sequences in plastic spiking neural networks.NEW JOURNAL OF PHYSICS,17(3),035006.
MLA Huang, Xuhui,et al."Different propagation speeds of recalled sequences in plastic spiking neural networks".NEW JOURNAL OF PHYSICS 17.3(2015):035006.
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