Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, real-time drift adaptation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates closed-loop communication among living and artificial model neurons and connections, and contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

中文翻译：

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自动适应模型神经元和连接，以构建具有生命网络的混合电路。

通过在活细胞与模型神经元和突触之间建立单向或双向相互作用而建立的混合电路是研究神经元，突触和神经网络动力学的有效方法。但是，混合电路技术在神经科学研究的背景下没有得到充分利用，这主要是由于实现和调整这种类型的交互作用固有的困难。在本文中，我们提出了一组用于在具有活体神经网络的混合电路的创建中自动适应模型神经元和连接的算法。该算法执行模型时间和幅度缩放，实时漂移自适应，目标驱动的突触和模型调整/校准以及自动参数映射。这些算法已在RTHybrid中实现，RTHybrid是一个开放源代码库，可处理严格的实时约束。我们通过在中央模式发生器中构建混合电路来提供验证示例。验证实验的结果表明，所提出的动态适应性促进了活体和人工模型神经元和连接之间的闭环通信，并有助于表征系统动力学，实现控制，自动化实验方案并延长了制剂的寿命。