We present LTC-SE, an improved version of the Liquid Time-Constant (LTC)
neural network algorithm originally proposed by Hasani et al. in 2021. This
algorithm unifies the Leaky-Integrate-and-Fire (LIF) spiking neural network
model with Continuous-Time Recurrent Neural Networks (CTRNNs), Neural Ordinary
Differential Equations (NODEs), and bespoke Gated Recurrent Units (GRUs). The
enhancements in LTC-SE focus on augmenting flexibility, compatibility, and code
organization, targeting the unique constraints of embedded systems with limited
computational resources and strict performance requirements. The updated code
serves as a consolidated class library compatible with TensorFlow 2.x, offering
comprehensive configuration options for LTCCell, CTRNN, NODE, and CTGRU
classes. We evaluate LTC-SE against its predecessors, showcasing the advantages
of our optimizations in user experience, Keras function compatibility, and code
clarity. These refinements expand the applicability of liquid neural networks
in diverse machine learning tasks, such as robotics, causality analysis, and
time-series prediction, and build on the foundational work of Hasani et al.Comment: 11 pages, 5 figures, 5 tables, This research work is partially drawn
from the MSc thesis of Michael B. Khani. arXiv admin note: text overlap with
arXiv:2006.04439 by other author