Neural Functional Connectivity Reconstruction with Second‐Order Memristor Network
Neural Functional Connectivity Reconstruction with Second‐Order Memristor Network
复制标题
DOI：
10.1002/aisy.202000276
复制
发表时间：
2021-05
影响因子：
7.4
通讯作者：
Yuting Wu;John Moon;Xiaojian Zhu;W. Lu
中科院分区：
文献类型：
--
作者：
Yuting Wu;John Moon;Xiaojian Zhu;W. Lu
神经记录技术的进步促进了同时记录的神经元数量的快速增长，为研究神经回路内部的相互作用和动力学开辟了新的可能性。然而，高记录通道数对数据分析提出了重大挑战，因为所需的时间和计算资源随着数据量超线性增长。在此，分析了使用二阶忆阻器网络来真实的实时重建神经功能连接的可行性。由忆阻器设备的内部动力学原生实现的尖峰定时依赖可塑性导致以无监督的方式成功发现模拟神经回路的突触前和突触后尖峰之间的时间相关性。该系统在考虑间接连接、突触权重、传输延迟、连接密度等参数设置的情况下，具有较高的分类精度，并能够捕获动态连接演化。系统评估了器械非理想因素对检测准确度的影响，系统对初始重量随机性、周期间和器械间变化具有鲁棒性。所提出的方法允许将神经连接直接映射到人工忆阻器网络上，并且可以导致高密度神经记录系统和潜在的直接耦合生物人工网络的高效前端数据分析。
The advances of neural recording techniques have fostered rapid growth of the number of simultaneously recorded neurons, opening up new possibilities to investigate the interactions and dynamics inside neural circuitry. The high recording channel counts, however, pose significant challenges for data analysis because the required time and computational resources grow superlinearly with the data volume. Herein, the feasibility of real‐time reconstruction of neural functional connectivity using a second‐order memristor network is analyzed. Spike‐timing‐dependent plasticity, natively implemented by the internal dynamics of the memristor device, leads to the successful discovery of temporal correlations between pre‐ and postsynaptic spikes of the simulated neural circuits in an unsupervised fashion. The proposed system demonstrates high classification accuracy under a wide range of parameter settings considering indirect connections, synaptic weights, transmission delays, connection density, and so on, and enables the capturing of dynamic connectivity evolutions. The influence of device nonideal factors on detection accuracy is systematically evaluated, and the system shows robustness to initial weight randomness, and cycle‐to‐cycle and device‐to‐device variations. The proposed method allows direct mapping of neural connectivity onto the artificial memristor network and can lead to efficient front‐end data analysis of high‐density neural recording systems and potentially directly coupled bioartificial networks.