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NS-Copilot is an LLM-driven multi-agent system designed to streamline and enhance neuroscience analysis by autonomously managing end-to-end workflows across various tasks. By integrating domain-specific pre-trained models and accommodating key modalities like EEG and extracellular spike data, it addresses the interdisciplinary barriers that have limited AI's impact in neuroscience. Evaluated on benchmarks related to Alzheimer's disease, Parkinson's disease, and working memory spike decoding, NS-Copilot consistently outperformed strong baselines, showcasing its effectiveness in facilitating complex analyses without relying on dataset-specific heuristics.
NS-Copilot outperforms traditional methods in neuroscience analysis by autonomously orchestrating specialized agents, bridging the gap between AI and complex biological data.
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.
