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海兔缩鳃反射回路的逼真模拟:回路元件在产生运动输出中的作用。

Realistic simulation of the Aplysia siphon-withdrawal reflex circuit: roles of circuit elements in producing motor output.

作者信息

Lieb J R, Frost W N

机构信息

Department of Neurobiology and Anatomy, University of Texas, Houston Health Science Center 77225, USA.

出版信息

J Neurophysiol. 1997 Mar;77(3):1249-68. doi: 10.1152/jn.1997.77.3.1249.

DOI:10.1152/jn.1997.77.3.1249
PMID:9084594
Abstract

The circuitry underlying the Aplysia siphon-elicited siphon-withdrawal reflex has been widely used to study the cellular substrates of simple forms of learning and memory. Nonetheless, the functional roles of the different neurons and synaptic connections modified with learning have yet to be firmly established. In this study we constructed a realistic computer simulation of the best-understood component of this network to better understand how the siphon-withdrawal circuit works. We used an integrate-and-fire scheme to simulate four neuron types (LFS, L29, L30, L34) and 10 synaptic connections. Each of these circuit components was individually constructed to match the mean or typical example of its biological counterpart on the basis of group measurements of each circuit element. Once each cell and synapse was modeled, its free parameters were fixed and not subject to further manipulation. The LFS motor neurons respond to sensory input with a brief phasic burst followed by a long-lasting period of tonic firing. We found that the assembled model network responded to sensory input in a qualitatively similar fashion, suggesting that many of the interneurons important for producing the LFS firing response have now been identified. By selectively removing different circuit elements, we determined the contribution of each of the LFS firing pattern. Our first finding was that the monosynaptic sensory neuron to motor neuron pathway contributed only to the initial brief burst of the LFS firing response, whereas the polysynaptic pathway determined the overall duration of LFS firing. By making more selective deletions, we found that the circuit elements responsible for transforming brief sensory neuron discharges into long-lasting LFS firing were the slow components of the L29-LFS fast/slow excitatory postsynaptic potentials. The inhibitory L30 neurons exerted a significant braking action on the flow of excitatory information through the circuit. Interestingly, L30 lost its ability to reduce the duration of LFS firing at high stimulus intensities. This was found to be due to the intrinsic nature of L30's current-frequency relationship. Some circuit elements, including interneuron L34, and the electrical coupling between L29 and L30 were found to have little impact when subtracted from the network. These results represent a detailed dissection of the functional roles of the different elements of the siphon-elicited siphon-withdrawal circuit in Aplysia. Because many vertebrate and invertebrate circuits perform similar tasks and contain similar information processing elements, aspects of these results may be of general significance for understanding the function of motor networks. In addition, because several sites in this network store learning-related information, these results are relevant to elucidating the functional significance of the distributed storage of learned information in Aplysia.

摘要

海兔虹吸管诱发的虹吸管退缩反射的神经回路已被广泛用于研究简单形式的学习和记忆的细胞基础。尽管如此,不同神经元和因学习而改变的突触连接的功能作用尚未得到确凿证实。在本研究中,我们构建了该网络中最易理解组件的逼真计算机模拟,以更好地了解虹吸管退缩回路的工作方式。我们使用积分发放方案来模拟四种神经元类型(LFS、L29、L30、L34)和10个突触连接。这些回路组件中的每一个都是根据对每个回路元件的群体测量单独构建的,以匹配其生物学对应物的平均值或典型示例。一旦对每个细胞和突触进行建模,其自由参数就会固定下来,不再进行进一步操作。LFS运动神经元对感觉输入的反应是短暂的相位爆发,随后是长时间的紧张性放电。我们发现组装好的模型网络对感觉输入的反应在性质上相似,这表明许多对产生LFS放电反应很重要的中间神经元现在已经被识别出来。通过选择性地去除不同的回路元件,我们确定了每种LFS放电模式的贡献。我们的第一个发现是,单突触感觉神经元到运动神经元的通路仅对LFS放电反应的初始短暂爆发有贡献,而多突触通路决定了LFS放电的总持续时间。通过进行更具选择性的删除,我们发现负责将短暂的感觉神经元放电转化为持久的LFS放电的回路元件是L29-LFS快/慢兴奋性突触后电位的慢成分。抑制性L30神经元对通过该回路的兴奋性信息流施加了显著的制动作用。有趣的是,在高刺激强度下,L30失去了缩短LFS放电持续时间的能力。这被发现是由于L30电流-频率关系的内在性质。一些回路元件,包括中间神经元L34,以及L29和L30之间的电耦合,从网络中减去时被发现影响很小。这些结果代表了对海兔虹吸管诱发的虹吸管退缩回路中不同元件功能作用的详细剖析。由于许多脊椎动物和无脊椎动物的回路执行类似的任务并包含类似的信息处理元件,这些结果的某些方面可能对理解运动网络的功能具有普遍意义。此外,由于该网络中的几个位点存储与学习相关的信息,这些结果与阐明海兔中学习信息分布式存储的功能意义相关。

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