Effects of synaptic integration on the dynamics and computational performance of spiking neural network
Effects of synaptic integration on the dynamics and computational performance of spiking neural network
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DOI：
10.1007/s11571-020-09572-y
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发表时间：
2020-02-19
影响因子：
3.7
通讯作者：
Xue, Fangzheng
中科院分区：
文献类型：
--
作者：
Li, Xiumin;Luo, Shengyuan;Xue, Fangzheng
大脑中的神经元接收来自其他神经元的数千个突触输入。神经元通过突触整合对传入信息进行加工，是生物神经网络中一种重要的信息加工机制。由突触前神经元的脉冲序列整合而成的突触电流具有复杂的非线性动力学，这赋予了神经元显著的计算能力。然而，在许多神经网络的计算研究中，外部输入电流通常被简单地视为静态的直流电。本文详细研究了突触电流和噪声外电流对脉冲神经网络的动态特性及其计算能力的影响。我们的研究结果表明，由于突触的非线性整合，快速和慢速兴奋性突触电流都比平均强度相同的噪声电流具有更复杂和振荡的波动。由此可见，由突触外电流驱动的网络表现出比噪声外电流驱动的网络更为复杂的动态特性。有趣的是，网络活动的增强有利于信息的传递，这进一步得到了在液态机（LSM）网络上进行的两个计算任务的支持。具有突触外电流的LSM在非线性拟合和模式分类方面都明显优于具有噪声外电流的LSM。突触整合可以显著提高LSM的活动模式熵和计算性能。我们的研究结果表明，非线性突触整合的复杂动力学在神经网络的计算能力中起着至关重要的作用，应该在尖峰神经网络的建模研究中得到更广泛的考虑。
Neurons in the brain receive thousands of synaptic inputs from other neurons. This afferent information is processed by neurons through synaptic integration, which is an important information processing mechanism in biological neural networks. Synaptic currents integrated from spiking trains of presynaptic neurons have complex nonlinear dynamics which endow neurons with significant computational abilities. However, in many computational studies of neural networks, external input currents are often simply taken as a direct current that is static. In this paper, the influences of synaptic and noise external currents on the dynamics of spiking neural network and its computational capability have been investigated in detail. Our results show that due to the nonlinear synaptic integration, both of fast and slow excitatory synaptic currents have much more complex and oscillatory fluctuations than the noise current with the same average intensity. Thus network driven by synaptic external current exhibits remarkably more complex dynamics than that driven by noise external current. Interestingly, the enhancement of network activity is beneficial for information transmission, which is further supported by two computational tasks conducted on the liquid state machine (LSM) network. LSM with synaptic external current displays considerably better performance in both nonlinear fitting and pattern classification than that with noise external current. Synaptic integration can significantly enhance the entropy of activity patterns and computational performance of LSM. Our results demonstrate that the complex dynamics of nonlinear synaptic integration play a critical role in the computational abilities of neural networks and should be more broadly considered in the modelling studies of spiking neural networks.