Neural connectivity inference with spike-timing dependent plasticity network
Neural connectivity inference with spike-timing dependent plasticity network
复制标题
使用尖峰时序相关可塑性网络进行神经连接推理
DOI：
10.1007/s11432-021-3217-0
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lu, Wei D.
中科院分区：
文献类型：
--
作者：
Moon, John;Wu, Yuting;Zhu, Xiaojian;Lu, Wei D.
了解神经回路中的连接模式对于理解大脑的运作机制至关重要，因为它允许分析神经信号如何处理和流过神经系统。随着神经记录技术在通道大小和时间分辨率方面的最新进展，非常需要一种简单有效的系统来执行神经连接推断，这将使得能够处理高维神经活动记录数据并减少计算时间和成本。在这项工作中，我们表明，尖峰定时依赖可塑性（STDP）算法可用于重建生物神经网络中的神经连接模式，具有更高的准确性和效率比基于神经网络的推理方法。生物启发的STDP学习规则在二阶忆阻器网络中本地实现，并用于估计神经连接的类型和方向。当被记录的神经尖峰序列刺激时，忆阻器器件电导由所提出的STDP学习规则调制，这反过来反映了尖峰的相关性和神经连接的可能性。通过补偿不同水平的神经活动，可以实现高度可靠的推理性能。所提出的方法提供了实时和本地学习，从而降低了计算成本/时间和对神经系统变化的强容忍度。
Knowing the connectivity patterns in neural circuitry is essential to understand the operating mechanism of the brain, as it allows the analysis of how neural signals are processed and flown through the neural system. With the recent advances in neural recording technologies in terms of channel size and time resolution, a simple and efficient system to perform neural connectivity inference is highly desired, which will enable the process of high dimensional neural activity recording data and reduction of the computational time and cost. In this work, we show that the spike-timing dependent plasticity (STDP) algorithm can be used to reconstruct neural connectivity patterns in a biological neural network, with higher accuracy and efficiency than statistic-based inference methods. The biologically inspired STDP learning rules are natively implemented in a second-order memristor network and are used to estimate the type and the direction of neural connections. When stimulated by the recorded neural spike trains, the memristor device conductance is modulated by the proposed STDP learning rules, which in turn reflects the correlation of the spikes and the possibility of neural connections. By compensating for the different levels of neural activity, highly reliable inference performance can be achieved. The proposed approach offers real-time and local learning, resulting in reduced computational cost/time and strong tolerance to variations of the neural system.
影响因子：
16.6
作者：
Xiaojian Zhu;Qiwen Wang;Wei D. Lu
通讯作者：
Xiaojian Zhu;Qiwen Wang;Wei D. Lu
DOI：
--
发表时间：
2008
期刊：
--
影响因子：
--
作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
通讯作者：
Jonathan W. Pillow;Jonathon Shlens;Liam Paninski;A. Sher;A. Litke;E. Chichilnisky;E. Simoncelli
影响因子：
4.7
作者：
Lee, Seung Hwan;Moon, John;Lu, Wei D.
通讯作者：
Lu, Wei D.