# Learning Dynamics of Solitonic Optical Multichannel Neurons

## TL;DR

This paper studies how optical neurons using solitons in lithium niobate crystals learn, comparing single and multi-node setups for speed and efficiency.

## Contribution

The study introduces a novel analysis of learning dynamics in solitonic optical neurons with varying topological configurations.

## Key findings

- Single-node neurons learn faster and with lower energy use compared to multi-node structures.
- Multi-node neurons offer more response diversity and better mimic biological neural tissues.
- Optical parameters can modulate the plasticity of these devices for photonic neuromorphic applications.

## Abstract

This study provides an in-depth analysis of the learning dynamics of multichannel optical neurons based on spatial solitons generated in lithium niobate crystals. Single-node and multi-node configurations with different topological complexities (3 × 3, 4 × 4, and 5 × 5) were compared, assessing how the number of channels, geometry, and optical parameters affect the speed and efficiency of learning. The simulations indicate that single-node neurons achieve the desired imbalance more rapidly and with lower energy expenditure, whereas multi-node structures require higher intensities and longer timescales, yet yield a greater variety of responses, more accurately reproducing the functional diversity of biological neural tissues. The results highlight how the plasticity of these devices can be entirely modulated through optical parameters, paving the way for fully optical photonic neuromorphic networks in which memory and computation are co-localized, with potential applications in on-chip learning, adaptive routing, and distributed decision-making.