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.
journal_name
J Comput Neuroscijournal_title
Journal of computational neuroscienceauthors
Guo D,Li Cdoi
10.1007/s10827-010-0279-7subject
Has Abstractpub_date
2011-06-01 00:00:00pages
567-87issue
3eissn
0929-5313issn
1573-6873journal_volume
30pub_type
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journal_title：Journal of computational neuroscience
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journal_title：Journal of computational neuroscience
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journal_title：Journal of computational neuroscience
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journal_title：Journal of computational neuroscience
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journal_title：Journal of computational neuroscience
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pub_type： 杂志文章
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journal_title：Journal of computational neuroscience
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