The approximation capability of ANNs and their RNN instantiations, is strongly correlated with the number of parameters packed into these networks. However, the complexity barrier for human understanding, is arguably related to the number of neurons and synapses in the networks, and to the associated nonlinear transformations. In this paper we show that the use of biophysical synapses, as found in LTCs, have two main benefits. First, they allow to pack more parameters for a given number of neurons and synapses. Second, they allow to formulate the nonlinear-network transformation, as a linear system with state-dependent coefficients. Both increase interpretability, as for a given task, they allow to learn a system linear in its input features, that is smaller in size compared to the state of the art. We substantiate the above claims on various time-series prediction tasks, but we believe that our results are applicable to any feedforward or recurrent ANN.

翻译：人工神经网络（ANN）及其循环神经网络（RNN）实例的近似能力，与网络中所包含的参数数量密切相关。然而，人类理解的复杂性障碍，可以说与网络中的神经元和突触数量，以及相关的非线性变换有关。在本文中，我们表明，使用像液体时间常数网络（LTC）中的生物物理突触，有两个主要优势。首先，对于给定数量的神经元和突触，它们允许封装更多的参数。其次，它们允许将非线性网络变换，构建为一个具有状态依赖系数的线性系统。两者都增强了可解释性，因为对于给定的任务，它们允许学习一个在其输入特征上是线性的、规模比现有同类更小的系统。我们通过多种时间序列预测任务来证实上述主张，但我们认为我们的结果适用于任何前馈或循环ANN。