Signal propagation in feedforward neuronal networks with unreliable synapses
Signal propagation in feedforward neuronal networks with unreliable synapses
复制标题
具有不可靠突触的前馈神经元网络中的信号传播
DOI：
10.1007/s10827-010-0279-7
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发表时间：
2011-06-01
影响因子：
1.2
通讯作者：
Li, Chunguang
中科院分区：
文献类型：
--
作者：
Guo, Daqing;Li, Chunguang
本文系统地研究了全对全耦合的前馈神经网络中的同火传播和放电率传播。与大多数早期只考虑可靠突触连接的工作不同，在这项工作中，我们主要考察了不可靠突触对这两种类型神经活动传播的影响。我们首先研究由纯兴奋性神经元组成的网络。我们的结果表明，成功传递概率和兴奋性突触强度对这两类神经活动的传播都有很大影响，而这些突触参数的较好调整使所考虑的网络支持稳定的信号传播。研究还发现，噪声对这两种传播方式有显著但不同的影响。加性高斯白噪声有降低同火活动精度的趋势，而适当强度的噪声可以提高射速传播性能。进一步的仿真表明，所考虑的神经网络的传播动力学不仅简单地取决于每个神经元在某个时刻接收到的神经递质的平均数量，而且在很大程度上受到神经递质释放的随机效应的影响。其次，我们将我们的结果与相应的前馈神经网络中的结果进行了比较，这些网络连接着可靠的突触，但以随机耦合的方式连接。我们证实，在这两个不同的前馈神经网络模型中可以观察到一些差异。最后，我们研究了由兴奋性神经元和抑制性神经元组成的前馈神经元网络中的信号传播，并证明了抑制在所考虑的网络中也在信号传播中起着重要作用。
In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.