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

2026-09-01

AI Summary

为解决神经科学领域中AI应用的跨学科障碍，NS-Copilot利用大语言模型驱动的多代理系统，整合特定领域的预训练模型，实现对不同类型数据的自动化分析。

Abstract

AI is rapidly advancing neuroscience, yet many laboratories fail to fully unleash its potential due to significant interdisciplinary barriers. While pre-trained neural models for physiological data are progressing quickly, their heterogeneous architectures and modality-specific constraints hinder systematic integration, selection, and evaluation. Despite recent advances in large language model (LLM)-based agent systems for intelligent scientific applications, existing approaches often still lack the domain expertise required to effectively select and coordinate diverse neuroscience pre-trained models and handle unique data types in this domain. We present NS-Copilot, an LLM-driven multi-agent system for neuroscience analysis that autonomously supports end-to-end workflows for diverse professional tasks. It unifies domain-specific pre-trained models and supports key neuroscience modalities, including EEG and extracellular spike data, through a natural-language interface. Given raw data and a task description, NS-Copilot orchestrates agents with specialized roles for planning, adaptive control, code generation, and result synthesis, enabling analysis without dataset-specific heuristics. We evaluate NS-Copilot on neuroscience benchmarks spanning Alzheimer's disease, Parkinson's disease, and working memory spike decoding. Across 8 trials per task, the system consistently outperforms strong baselines on the primary metric, demonstrating the ability of NS-Copilot for effective and scalable neuroscience analysis.

Problem

• neuroscience
• AI
• interdisciplinary barriers
• pre-trained models
• large language model (LLM)

Innovation

• LLM-driven
• multi-agent system
• neuroscience analysis
• natural-language interface
• end-to-end workflows