论文标题
具有连续时间复发神经网络的有限状态计算的可兴奋网络
Excitable Networks for Finite State Computation with Continuous Time Recurrent Neural Networks
论文作者
论文摘要
连续的时间复发性神经网络（CTRNN）是耦合的普通微分方程的系统，这些系统非常简单，可以从生物学和机器学习观点中洞悉学习和计算。我们描述了一种在任意有向图上实现有限状态输入依赖性计算的直接建设性方法。构造的系统具有一个令人兴奋的网络吸引子，我们用许多示例来说明其动态。由此产生的CTRNN具有间歇性动力学：轨迹花费很长时间接近稳态，并在状态之间进行快速过渡。根据参数，状态之间的过渡可以是可兴奋的（输入或噪声需要超过阈值以诱导过渡）或自发（过渡发生，而没有输入或噪声）。在令人兴奋的情况下，我们显示兴奋性的阈值可以任意敏感。
Continuous time recurrent neural networks (CTRNN) are systems of coupled ordinary differential equations that are simple enough to be insightful for describing learning and computation, from both biological and machine learning viewpoints. We describe a direct constructive method of realising finite state input-dependent computations on an arbitrary directed graph. The constructed system has an excitable network attractor whose dynamics we illustrate with a number of examples. The resulting CTRNN has intermittent dynamics: trajectories spend long periods of time close to steady-state, with rapid transitions between states. Depending on parameters, transitions between states can either be excitable (inputs or noise needs to exceed a threshold to induce the transition), or spontaneous (transitions occur without input or noise). In the excitable case, we show the threshold for excitability can be made arbitrarily sensitive.
