title: Network evolution induced by asynchronous stimuli through spike-timing-dependent plasticity.
abstract: In sensory neural system, external asynchronous stimuli play an important role in perceptual learning, associative memory and map development. However, the organization of structure and dynamics of neural networks induced by external asynchronous stimuli are not well understood. Spike-timing-dependent plasticity (STDP) is a typical synaptic plasticity that has been extensively found in the sensory systems and that has received much theoretical attention. This synaptic plasticity is highly sensitive to correlations between pre- and postsynaptic firings. Thus, STDP is expected to play an important role in response to external asynchronous stimuli, which can induce segregative pre- and postsynaptic firings. In this paper, we study the impact of external asynchronous stimuli on the organization of structure and dynamics of neural networks through STDP. We construct a two-dimensional spatial neural network model with local connectivity and sparseness, and use external currents to stimulate alternately on different spatial layers. The adopted external currents imposed alternately on spatial layers can be here regarded as external asynchronous stimuli. Through extensive numerical simulations, we focus on the effects of stimulus number and inter-stimulus timing on synaptic connecting weights and the property of propagation dynamics in the resulting network structure. Interestingly, the resulting feedforward structure induced by stimulus-dependent asynchronous firings and its propagation dynamics reflect both the underlying property of STDP. The results imply a possible important role of STDP in generating feedforward structure and collective propagation activity required for experience-dependent map plasticity in developing in vivo sensory pathways and cortices. The relevance of the results to cue-triggered recall of learned temporal sequences, an important cognitive function, is briefly discussed as well. Furthermore, this finding suggests a potential application for examining STDP by measuring neural population activity in a cultured neural network.
authors.affiliation: College of Physics and Electronic Information, Huaibei Normal University, Huaibei, China ; Department of Physics, Centre for Nonlinear Studies and the Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
authors.forename: Wu-Jie
authors.initials: WJ
authors.lastname: Yuan
authors.affiliation: College of Physics and Electronic Information, Huaibei Normal University, Huaibei, China.
authors.forename: Jian-Fang
authors.initials: JF
authors.lastname: Zhou
authors.affiliation: Department of Physics, Centre for Nonlinear Studies and the Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong ; Beijing Computational Science Research Center, Beijing, China ; Research Centre, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China.
authors.forename: Changsong
authors.initials: C
authors.lastname: Zhou
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000344:Afferent Pathways / Q000502:physiology*; D003198:Computer Simulation; D004558:Electric Stimulation; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D013997:Time Factors