Quantitative Analysis of Dynamical Complexity in Cultured Neuronal Network Models for Reservoir Computing Applications
Quantitative Analysis of Dynamical Complexity in Cultured Neuronal Network Models for Reservoir Computing Applications
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DOI：
10.1109/ijcnn.2019.8852207
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
S. Moriya;Hideaki Yamamoto;A. Hirano-Iwata;Shigeru Kubota;S. Sato
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文献类型：
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作者：
S. Moriya;Hideaki Yamamoto;A. Hirano-Iwata;Shigeru Kubota;S. Sato
水库计算是一种机器学习范式，被提出作为大脑皮层信息处理的模型。它使用大规模递归神经网络的时空动态来处理信息，并有望提高神经形态计算系统的功率效率和速度。先前的理论研究表明，大脑网络表现出完全一致性和随机放电的中间状态，这适合于水库计算。然而，如何水库性能的影响，特别是在最近的大脑网络的连接组学分析，仍然不清楚。在这里，我们构建了集成和火灾神经元的模块化网络，并研究了模块化结构和兴奋抑制神经元比例对网络动力学的影响。动态进行了评估的基础上，以下三个措施：同步爆发频率，平均相关性，和功能的复杂性。我们发现，在一个纯粹的兴奋性网络，复杂性是独立的模块化的网络。另一方面，具有抑制性神经元的网络在模块性高时表现出复杂的网络活性。我们的研究结果揭示了大脑网络中水库性能的一个基本方面，有助于生物启发水库计算系统的设计。
Reservoir computing is a machine learning paradigm that was proposed as a model of cortical information processing in the brain. It processes information using the spatiotemporal dynamics of a large-scale recurrent neural network and is expected to improve power efficiency and speed in neuromorphic computing systems. Previous theoretical investigation has shown that brain networks exhibit an intermediate state of full coherence and random firing, which is suitable for reservoir computing. However, how reservoir performance is influenced by connectivity, especially which revealed in recent connectomics analysis of brain networks, remains unclear. Here, we constructed modular networks of integrate-and-fire neurons and investigated the effect of modular structure and excitatory-inhibitory neuron ratio on network dynamics. The dynamics were evaluated based on the following three measures: synchronous bursting frequency, mean correlation, and functional complexity. We found that in a purely excitatory network, the complexity was independent of the modularity of the network. On the other hand, networks with inhibitory neurons exhibited complex network activity when the modularity was high. Our findings reveal a fundamental aspect of reservoir performance in brain networks, contributing to the design of bio-inspired reservoir computing systems.