{"slug": "ns-copilot-an-llm-driven-agent-system-for-autonomous-neuroscience-analysis", "title": "NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis", "summary": "Researchers introduced NS-Copilot, an LLM-driven multi-agent system for autonomous neuroscience analysis that unifies domain-specific pre-trained models for EEG and extracellular spike data via a natural-language interface. In evaluations across benchmarks for Alzheimer's disease, Parkinson's disease, and working memory spike decoding, NS-Copilot outperformed strong baselines on the primary metric across 8 trials per task.", "body_md": "arXiv:2609.01971v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/ns-copilot-an-llm-driven-agent-system-for-autonomous-neuroscience-analysis", "canonical_source": "https://arxiv.org/abs/2609.01971", "published_at": "2026-09-03 04:00:00+00:00", "updated_at": "2026-09-03 04:25:49.544609+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["NS-Copilot", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/ns-copilot-an-llm-driven-agent-system-for-autonomous-neuroscience-analysis", "markdown": "https://wpnews.pro/news/ns-copilot-an-llm-driven-agent-system-for-autonomous-neuroscience-analysis.md", "text": "https://wpnews.pro/news/ns-copilot-an-llm-driven-agent-system-for-autonomous-neuroscience-analysis.txt", "jsonld": "https://wpnews.pro/news/ns-copilot-an-llm-driven-agent-system-for-autonomous-neuroscience-analysis.jsonld"}}