TY - GEN
T1 - The Dynamics of Bimodular Continuous Attractor Neural Networks with Moving Stimuli
AU - Yan, Min
AU - Zhang, Wen Hao
AU - Wang, He
AU - Wong, K. Y.Michael
N1 - Funding Information:
Acknowledgments. This work is supported by grants from the Research Grants Council of Hong Kong (grant numbers N HKUST606/12, 605813 and 16322616).
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - The single-layer continuous attractor neural network (CANN) model has been applied successfully to describe the tracking of moving stimuli of a single modality. Experimental evidence shows that stimuli of different modalities interact with each other in the neural system. To study these interaction effects, we generalize the single-module structure to a bimodular one. We found that when there is one static stimulus in one module and a moving one in the other, the network have very different behaviours depending on whether the inter-modular couplings are excitatory or inhibitory. We further compare the model with experimental observations that illustrate the interactions between two sensory modalities, such as the motion-bounce Illusion. Agreement between model and experimental results can be obtained for appropriate choice of parameters.
AB - The single-layer continuous attractor neural network (CANN) model has been applied successfully to describe the tracking of moving stimuli of a single modality. Experimental evidence shows that stimuli of different modalities interact with each other in the neural system. To study these interaction effects, we generalize the single-module structure to a bimodular one. We found that when there is one static stimulus in one module and a moving one in the other, the network have very different behaviours depending on whether the inter-modular couplings are excitatory or inhibitory. We further compare the model with experimental observations that illustrate the interactions between two sensory modalities, such as the motion-bounce Illusion. Agreement between model and experimental results can be obtained for appropriate choice of parameters.
KW - Continuous attractor neural networks
KW - Motion-Bounce Illusion
KW - Multisensory information processing
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U2 - 10.1007/978-3-319-70093-9_69
DO - 10.1007/978-3-319-70093-9_69
M3 - Conference contribution
AN - SCOPUS:85035121602
SN - 9783319700922
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 648
EP - 657
BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
A2 - Liu, Derong
A2 - Xie, Shengli
A2 - Li, Yuanqing
A2 - El-Alfy, El-Sayed M.
A2 - Zhao, Dongbin
PB - Springer Verlag
T2 - 24th International Conference on Neural Information Processing, ICONIP 2017
Y2 - 14 November 2017 through 18 November 2017
ER -