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.

中文翻译：

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突触整合对尖峰神经网络动力学和计算性能的影响。

大脑中的神经元从其他神经元接收成千上万的突触输入。神经元通过突触整合处理传入的信息，这是生物神经网络中重要的信息处理机制。从突触前神经元的突波串积分的突触电流具有复杂的非线性动力学，赋予神经元显着的计算能力。但是，在神经网络的许多计算研究中，通常将外部输入电流简单地视为静态直流电。本文详细研究了突触和噪声外电流对尖峰神经网络的动力学及其计算能力的影响。我们的结果表明，由于非线性突触整合，与具有相同平均强度的噪声电流相比，快速和慢速兴奋性突触电流都具有复杂得多的振荡波动。因此，由突触外部电流驱动的网络比由噪声外部电流驱动的网络表现出更为复杂的动力学。有趣的是，网络活动的增强对于信息传输是有益的，这在液态状态机（LSM）网络上执行的两个计算任务进一步得到支持。带有突触外部电流的LSM在非线性拟合和模式分类方面均比具有噪声外部电流的LSM具有更好的性能。突触整合可以显着增强LSM的活动模式和计算性能的熵。