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

• URL: http://arxiv.org/abs/2609.01971v1
• Date: Wed, 02 Sep 2026 00:57:05 GMT
• Title: NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis
• Abstract summary: NSCopilot is a large language model (LLM)-driven multi-agent system for neuroscience analysis.<n>It unifies domain-specific pre-trained models and supports key neuroscience modalities, including EEG and extracellular spike data.<n>It orchestrates agents with specialized roles for planning, adaptive control, code generation, and result.
• Score: 16.317509493719697
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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