title: Model predictions of features in microsaccade-related neural responses in a feedforward network with short-term synaptic depression.
abstract: Recently, the significant microsaccade-induced neural responses have been extensively observed in experiments. To explore the underlying mechanisms of the observed neural responses, a feedforward network model with short-term synaptic depression has been proposed [Yuan, W.-J., Dimigen, O., Sommer, W. and Zhou, C. Front. Comput. Neurosci. 7, 47 (2013)]. The depression model not only gave an explanation for microsaccades in counteracting visual fading, but also successfully reproduced several microsaccade-related features in experimental findings. These results strongly suggest that, the depression model is very useful to investigate microsaccade-related neural responses. In this paper, by using the model, we extensively study and predict the dependance of microsaccade-related neural responses on several key parameters, which could be tuned in experiments. Particularly, we provide a significant prediction that microsaccade-related neural response also complies with the property "sharper is better" observed in many contexts in neuroscience. Importantly, the property exhibits a power-law relationship between the width of input signal and the responsive effectiveness, which is robust against many parameters in the model. By using mean field theory, we analytically investigate the robust power-law property. Our predictions would give theoretical guidance for further experimental investigations of the functional role of microsaccades in visual information processing.
authors: College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China.
authors: Jian-Fang
authors: JF
authors: Zhou
authors: College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, 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: Wu-Jie
authors: WJ
authors: Yuan
authors: College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China.
authors: Zhao
authors: Z
authors: Zhou
authors: 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 100084, China.|Research Centre, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China.
authors: Changsong
authors: C
authors: Zhou
mesh_terms: D000465:Algorithms; D005403:Fixation, Ocular / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009473:Neuronal Plasticity; D010775:Photic Stimulation; D012438:Saccades / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology