Functional differentiation of a population of electrically coupled heterogeneous elements in a microcircuit

Kosai Sasaki, Elizabeth C. Cropper, Klaudiusz R. Weiss, Jian Jing

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Although electrical coupling is present in many microcircuits, the extent to which it will determine neuronal firing patterns and network activity remains poorly understood. This is particularly true when the coupling is present in a population of heterogeneous, or intrinsically distinct, circuit elements. We examine this question in the Aplysia californica feeding motor network in five electrically coupled identified cells, B64, B4/5, B70, B51, and a newly identified interneuron B71. These neurons exhibit distinct activity patterns during the radula retraction phase of motor programs. In a subset of motor programs, retraction can be flexibly extended by adding a phase of network activity (hyper-retraction). This is manifested most prominently as an additional burst in the radula closure motoneuron B8. Two neurons that excite B8 (B51 and B71) and one that inhibits it (B70) are active during hyper-retraction. Consistent with their near synchronous firing, B51 and B71 showed one of the strongest coupling ratios in this group of neurons. Nonetheless, by manipulating their activity, we found that B51 preferentially acted as a driver of B64/B71 activity, whereas B71 played a larger role in driving B8 activity. In contrast, B70 was weakly coupled to other neurons and its inhibition of B8 counteracted the excitatory drive to B8. Finally, the distinct firing patterns of the electrically coupled neurons were fine-tuned by their intrinsic properties and the largely chemical cross-inhibition between some of them. Thus, the small microcircuit of the Aplysia feeding network is advantageous in understanding how a population of electrically coupled heterogeneous neurons may fulfill specific network functions.

Original languageEnglish
Pages (from-to)93-105
Number of pages13
JournalJournal of Neuroscience
Volume33
Issue number1
DOIs
StatePublished - 2 Jan 2013

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