TNLearn: An Open Source Python Package for Task-based Neurons
cs.LG, cs.AI
Submitted: 2026-09-23
Updated: 2026-09-23
Comments: 24 pages, 6 figures, 6 tables
Code: https://github.com/NewT123-WM/tnlearn
License: http://creativecommons.org/licenses/by/4.0/
The gist: The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks.
Terminology
Abstract
The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The concept of task-based neurons represents a paradigm shift compared to task-based architectures. It argues that solving a specific problem requires customized neurons, as task-based neurons capture useful prior knowledge from task-related data. To facilitate the use of task-based neurons in scientific research and industrial applications, we introduce TNLearn, an open-source Python package that provides automated construction of task-based neurons and networks, enabling smooth training of task-based networks. Comprehensive documentation, including technical exposition, API reference, and representative examples, is available online. TNLearn is open-sourced at https://github.com/NewT123-WM/tnlearn and has become a PyTorch ecosystem project.
Sources
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- Learning Sparse Neural Networks through $L_0$ Regularization
- Highway Networks
- DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience
- QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration
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