Liquid Neural Networks

1. Introduction
2. Architecture Overview
3. Continuous-Time Dynamics
- 3.1. ODE-based State Update
- 3.2. Time Constant Adaptation
4. Comparison with Traditional Networks
5. C. elegans Inspiration
6. Training Pipeline
7. Applications
8. Implementation Considerations
- 8.1. Hybrid Clojure/Python Architecture
9. Key Papers
10. Resources

1. Introduction

Liquid Neural Networks (LNNs) are continuous-time neural networks inspired by the nervous system of C. elegans, achieving complex tasks with remarkably few neurons (19-302 vs millions in traditional networks).

2. Architecture Overview

3. Continuous-Time Dynamics

3.1. ODE-based State Update

3.2. Time Constant Adaptation

4. Comparison with Traditional Networks

5. C. elegans Inspiration

6. Training Pipeline

7. Applications

8. Implementation Considerations

8.1. Hybrid Clojure/Python Architecture

9. Key Papers

Hasani et al., "Liquid Time-constant Networks" (2021)
Lechner et al., "Neural Circuit Policies" (2020)
White et al., "C. elegans Connectome" (1986)

10. Resources