NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training

2026-05-03

AI Summary

This work addresses the opacity of neural network training, which poses significant challenges for beginners. To enhance interpretability, the authors developed an interactive web-based visualization tool that, for the first time, synchronously displays the dynamic correspondence between weight updates and activation signals within a single training iteration, while also rendering neuron-specific update equations in real time. The system enables fine-grained tracking and dynamic visualization of the training process, allowing users to interactively configure and observe forward and backward propagation, activation values, and loss evolution. In a user study with 31 participants, the tool achieved a System Usability Scale (SUS) score of 80.97—indicating excellent usability—with mean rankings of 2.47 for clarity and 2.23 for practicality; over 70% of users reported that it substantially improved their understanding of neural network training mechanisms.

Abstract

Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports real-time exploration of fully connected neural network training. Users can configure network architecture, activation functions, learning rates, and datasets, then observe activations, weight updates, and loss progression. NeuroViz visualizes weight changes in direct correspondence with activation signals in both forward and backward passes, enabling users to distinguish pre- and post-update states within individual epochs and view dynamically updating per-neuron equations. We conduct a comparative user study with 31 participants against six established visualization tools and we achieved the highest usability score (SUS 80.97, in the 'excellent' range), with mean rankings of 2.47 for clarity and 2.23 for usefulness (lower is better). Over 70% of participants reported that the visualizations substantially increased their perception of neural network training transparency. The implemented instance is accessible at https://neuroviz.org.

Problem

• neural network interpretability
• interactive visualization
• training transparency
• forward and backward passes
• real-time exploration

Innovation

• real-time visualization
• forward-backward pass
• interactive neural network training
• weight-activation correspondence
• dynamic neuron equations