Synaptic mechanisms for motor variability in a feedforward network

Guo Zhang, Ke Yu, Tao Wang, Ting Ting Chen, Wang Ding Yuan, Fan Yang, Zi Wei Le, Shi Qi Guo, Ying Yu Xue, Song An Chen, Zhe Yang, Feng Liu, Elizabeth C. Cropper, Klaudiusz R. Weiss, Jian Jing

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Behavioral variability often arises from variable activity in the behavior-generating neural network. The synaptic mechanisms underlying this variability are poorly understood. We show that synaptic noise, in conjunction with weak feedforward excitation, generates variable motor output in the Aplysia feeding system. A command-like neuron (CBI-10) triggers rhythmic motor programs more variable than programs triggered by CBI-2. CBI-10 weakly excites a pivotal pattern-generating interneuron (B34) strongly activated by CBI-2. The activation properties of B34 substantially account for the degree of program variability. CBI-10- and CBI-2-induced EPSPs in B34 vary in amplitude across trials, suggesting that there is synaptic noise. Computational studies show that synaptic noise is required for program variability. Further, at network state transition points when synaptic conductance is low, maximum program variability is promoted by moderate noise levels. Thus, synaptic strength and noise act together in a nonlinear manner to determine the degree of variability within a feedforward network.

Original languageEnglish
Article numbereaba4856
JournalScience advances
Issue number25
StatePublished - Jun 2020


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