Estimation of network structures only from spike sequences |
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| Authors: | Kaori Kuroda Tohru Ashizawa Tohru Ikeguchi |
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| Affiliation: | 1. Graduate School of Science and Engineering, Saitama University - 225 Shimo-Ohkubo, Sakura-ku, Saitama-city 338-8570, Japan;2. Saitama University Brain Science Institute - 225 Shimo-Ohkubo, Sakura-ku, Saitama-city 338-8570, Japan;1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Nanjing Artillery Academy, Nanjing 211132, China;3. State Key Laboratory of Millimeter Waves of Southeast University, Nanjing Jiangsu 210096, China;1. Departamento de Física, Facultad de Ciencias Exactas, UNLP Calle 49 y 115. C.C. 67 (1900), La Plata, Argentina;2. IFLYSIB, CONICET & Universidad Nacional de La Plata, Calle 59-789, 1900 La Plata, Argentina;3. IFLP, CONICET & Universidad Nacional de La Plata, Calle 49 y 115, 1900 La Plata, Argentina;4. Department of Mathematics, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom;5. Department of Bioengineering, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom;1. School of Mechanical Engineering, Heilongjiang University of Science and Technology, Harbin 150022, PR China;2. College of Light Industry, Harbin University of Commerce, Harbin 150028, PR China;1. State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi’an Jiaotong University, Xi’an 710049, China;2. Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands;3. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, People’s Republic of China;4. Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an 710049, China |
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| Abstract: | A neuron, the fundamental element of neural systems, interacts with other neurons, often producing very complicated behavior. To analyze, model, or predict such complicated behavior, it is important to understand how neurons are connected as well as how they behave. In this paper, we propose two measures, the spike time metric coefficient and the partial spike time metric coefficient, to estimate the network structure, that is, the topological connectivity between neurons. The proposed measures are based on the spike time metric and partialization analysis. To check the validity, we applied the proposed measures to asynchronous spike sequences that are produced by a mathematical neural network model. It was found that the proposed measure has high performance for estimating the network structures even though the structures have a complex topology such as a small-world structure or a scale-free structure. |
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