Modeling of topology-dependent neural network plasticity induced by activity-dependent electrical stimulation.
Modeling of topology-dependent neural network plasticity induced by activity-dependent electrical stimulation.
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由活动相关的电刺激引起的拓扑相关的神经网络可塑性的建模。
DOI：
10.1109/ner.2013.6696063
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Barbour,DennisL
中科院分区：
文献类型：
--
作者：
Ni,Ruiye;Ledbetter,NoahM;Barbour,DennisL
Activity-dependent electrical stimulation can induce cerebrocortical reorganization in vivo by activating brain areas using stimulation derived from the statistics of neural or muscular activity. Due to the nature of synaptic plasticity, network topology is likely to influence the effectiveness of this type of neuromodulation, yet its effect under different network topologies is unclear. To address this issue, we simulated small-scale three-neuron networks to explore topology-dependent network plasticity. The induced neuroplastic changes were evaluated by network coherence and unit-pair mutual information measures. We demonstrated that involvement of monosynaptic feedforward and reciprocal connections is more likely to lead to persistent decreased network coherence and increased network mutual information independent of the global network topology. On the contrary, disynaptic feedforward connections exhibit heterogeneous coherence and unit-pair mutual information sensitivity that depends strongly upon the network context.
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期刊：
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