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Signal propagation in feedforward neuronal networks with unreliable synapses

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Abstract

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.

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Notes

  1. An anonymous reviewer kindly reminded us that there might be a relevant abstract (Trommershäuser and Diesmann 2001) discussing the effect of synaptic variability on the synchronization dynamics in feedforward cortical neural networks, but the abstract itself does not contain the results presumably presented on the poster and also the follow-up publications do not exist.

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Acknowledgements

We thank Feng Chen, Yuke Li, Qiuyuan Miao, Xin Wei and Qunxian Zheng for valuable comments on an early version of this manuscript. We gratefully acknowledge the anonymous reviewers for providing useful comments and suggestions, which greatly improved our paper. We also sincerely thank one reviewer for reminding us of a critical citation (Trommershäuser and Diesmann 2001). This work is supposed by the National Natural Science Foundation of China (Grant No. 60871094), the Foundation for the Author of National Excellent Doctoral Dissertation of PR China, and the Fundamental Research Funds for the Central Universities (Grant No. 1A5000-172210126). Daqing Guo would also like to thank the award of the ongoing best PhD thesis support from the University of Electronic Science and Technology of China.

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  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    Daqing Guo

  2. Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Chunguang Li

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  1. Daqing Guo
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Guo, D., Li, C. Signal propagation in feedforward neuronal networks with unreliable synapses. J Comput Neurosci 30, 567–587 (2011). https://doi.org/10.1007/s10827-010-0279-7

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