Functionality of neural dynamics induced by long-tailed synaptic distribution in reservoir computing
Functionality of neural dynamics induced by long-tailed synaptic distribution in reservoir computing
复制标题
DOI：
10.1587/nolta.14.342
复制
发表时间：
2023-01-01
影响因子：
0.5
通讯作者：
Kurikawa, Tomoki
中科院分区：
文献类型：
--
作者：
Matsumoto, Ibuki;Nobukawa, Sou;Kurikawa, Tomoki
在大脑皮层中，兴奋性突触后电位（EPSP）呈现长尾分布。尽管 EPSP 诱导丰富的神经活动，但它们对大脑功能的贡献仍不清楚。因此，本研究通过构建储库计算（RC）模型并比较有和没有强权重的RC之间的非线性时间序列的记忆容量和预测精度，评估了长尾突触权重引起的动力学效果。结果表明，强权重通过伽马带动态神经活动显着增强了 RC 性能。这种机制可能支持实际大脑网络中的认知过程。
In the cerebral cortex, excitatory postsynaptic potentials (EPSPs) exhibit a longtailed distribution. Although EPSPs induce rich neural activity, their contributions to brain function remain unclear. Therefore, this study evaluated the effect of the dynamics induced by long-tailed synaptic weights by constructing a reservoir computing (RC) model and comparing the memory capacity and predictive accuracy for nonlinear time-series between RCs, with and without strong weights. The results revealed that strong weights significantly enhance the RC performance through gamma-band dynamic neural activity. This mechanism may support the cognitive processes in the actual brain network.