Learning Times Required to Identify the Stimulated Position and Shortening of Propagation Path by Hebb’s Rule in Neural Network

Shun Sakuma; Yuko Mizuno-Matsumoto; Yoshi Nishitani; Shinichi Tamura; Shun Sakuma; Yuko Mizuno-Matsumoto; Yoshi Nishitani; Shinichi Tamura

doi:10.3934/Neuroscience.2017.4.238

AIMS Neuroscience

2017, Volume 4, Issue 4: 238-253. doi: 10.3934/Neuroscience.2017.4.238

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Research article

Learning Times Required to Identify the Stimulated Position and Shortening of Propagation Path by Hebb’s Rule in Neural Network

1.
Graduate School of Applied Informatics, University of Hyogo, Kobe 650-0047, Japan;
2.
Department of Radiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan;
3.
NBL Technovator Co., Ltd, 631 Shindachimakino, Sennan 590-0522, Japan

Received: 24 August 2017 Accepted: 09 November 2017 Published: 29 November 2017

To deepen the understanding of the human brain, many researchers have created a new way of analyzing neural data. In many previous studies, researchers have examined neural networks from a macroscopic point of view, based on neuronal firing patterns. On the contrary, we have studied neural networks locally, in order to understand their communication strategies. To understand information processing in the brain, we simulated the firing activities of neural networks in a 9 × 9 two-dimensional neural network to analyze spike behavior. In this research study, we used two kinds of learning processes. As the main learning process, we implemented the learning process to identify the stimulated position. As the subsidiary one, we implemented Hebb’s learning rule which changes weight between neurons. Three channels with transmission and reception were preset, each of which has a different distance and direction. When all three channels succeeded in identifying the source stimulation in the receiving neuron group, it was regarded as an overall success and the learning was termed as successful. Furthermore, in order to see the effect of the second learning procedure, we elucidated the average of necessary learning times in each channel type and compared the firing propagation time of the first trial and an overall successful trial, in each channel. We found that the firing path after learning is shorter than the firing path before learning. Therefore, we deduced that Hebb’s rule contributes to shortening the firing path. Thus, Hebb’s rule contributes to speeding up communication in a neuronal network.
- spike wave,
- Hebb’s rule,
- learning times,
- firing propagation times,
- neural network
Citation: Shun Sakuma, Yuko Mizuno-Matsumoto, Yoshi Nishitani, Shinichi Tamura. Learning Times Required to Identify the Stimulated Position and Shortening of Propagation Path by Hebb’s Rule in Neural Network[J]. AIMS Neuroscience, 2017, 4(4): 238-253. doi: 10.3934/Neuroscience.2017.4.238

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Abstract

To deepen the understanding of the human brain, many researchers have created a new way of analyzing neural data. In many previous studies, researchers have examined neural networks from a macroscopic point of view, based on neuronal firing patterns. On the contrary, we have studied neural networks locally, in order to understand their communication strategies. To understand information processing in the brain, we simulated the firing activities of neural networks in a 9 × 9 two-dimensional neural network to analyze spike behavior. In this research study, we used two kinds of learning processes. As the main learning process, we implemented the learning process to identify the stimulated position. As the subsidiary one, we implemented Hebb’s learning rule which changes weight between neurons. Three channels with transmission and reception were preset, each of which has a different distance and direction. When all three channels succeeded in identifying the source stimulation in the receiving neuron group, it was regarded as an overall success and the learning was termed as successful. Furthermore, in order to see the effect of the second learning procedure, we elucidated the average of necessary learning times in each channel type and compared the firing propagation time of the first trial and an overall successful trial, in each channel. We found that the firing path after learning is shorter than the firing path before learning. Therefore, we deduced that Hebb’s rule contributes to shortening the firing path. Thus, Hebb’s rule contributes to speeding up communication in a neuronal network.

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