#artificial intelligence #machine learning Preprint

TNLearn: An Open Source Python Package for Task-based Neurons

Meng Wang Tie-Yun Li Jun-Tong Fan Han-Yu Pei Jing-Xiao Liao Yao Yang Jian-Wei Ma Feng-Lei Fan

Sep 2026 · 0 citations · 36 references

Computer Science

TL;DR

TNLearn, an open-source Python package that provides automated construction of task-based neurons and networks, enabling smooth training of task-based networks, is introduced.

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.

DOI arXiv Semantic Scholar

View source

Similar papers

Open access Sep 2026

NeuralRNN: a unified framework for recurrent neural network methods in cognitive neuroscience

This work introduces NeuralRNN, an open-source Python framework that unifies these paradigms and model variants in a general-purpose pipeline, and provides an integrated and extensible model library spanning generic, structure-constrained, gain-modulated, and gated RNNs.

Hong-Hua Chen, Nai Ding · bioRxiv · 0 citations

Open access Oct 2026

Neural circuit mechanisms for multi-tasking

Neural activity during performance of a cognitive or motor task is often described as lying on a low-dimensional manifold in the space of all possible activity patterns. When multiple tasks are performed, an individual brain region’s activity may generate multiple such manifolds. Several models have been introduced to...

Manuel Beirán, Owen Marschall, Klavdia Zemlianova et al. · bioRxiv · 0 citations

#natural language process... Preprint Sep 2026

NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis

NS-Copilot is presented, an LLM-driven multi-agent system for neuroscience analysis that autonomously supports end-to-end workflows for diverse professional tasks and unifies domain-specific pre-trained models and supports key neuroscience modalities, including EEG and extracellular spike data, through a natural-langua...

Wu Liu, Yi-Ran Qiao, Lingfeng Hou et al. · 1 citation

#machine learning Preprint Sep 2026

Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks

This paper addresses formal explainability of classifying neural networks by introducing a flexible symbolic framework for an efficient, guided computation of explanations of the NN behavior, parametrized by the activations of internal neurons, and using logical engines such as SMT solvers.

Tomáš Kolárik, Faezeh Labbaf, Fabrizio Leopardi et al. · 0 citations

#natural language process... Preprint Sep 2026

Large Language Models with At Most One Spike per Neuron

This work constructs a fully TTFS-based SNN architecture and train it end-to-end, and introduces a reference-based strategy specifically to encode the four core LLM components: embedding layers, layer normalization, attention-related operations and dropout.

Zhuo-Ya Zhao, Parsa Omidi, A. Jafari et al. · 0 citations