Liquid Time-constant Recurrent Neural Networks as Universal Approximators [article]

Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
2018 arXiv   pre-print
In this paper, we introduce the notion of liquid time-constant (LTC) recurrent neural networks (RNN)s, a subclass of continuous-time RNNs, with varying neuronal time-constant realized by their nonlinear synaptic transmission model. This feature is inspired by the communication principles in the nervous system of small species. It enables the model to approximate continuous mapping with a small number of computational units. We show that any finite trajectory of an n-dimensional continuous
more » ... cal system can be approximated by the internal state of the hidden units and n output units of an LTC network. Here, we also theoretically find bounds on their neuronal states and varying time-constant.
arXiv:1811.00321v1 fatcat:wmyf54nl5jghrhxpilswkd3ice