SpikeLab: Agentic tools for spike data analysis

Area of Science:

• Neuroscience
• Computational Biology
• Artificial Intelligence

Background:

• Large language models (LLMs) offer potential for scientific analysis but risk silent methodological errors without domain-specific structure.
• Unassisted LLMs can lead to irreproducible results and unreported analytical decisions in complex data.
• Electrophysiology data analysis demands high accuracy and reproducibility, areas where current LLMs may falter.

Purpose of the Study:

• To introduce SpikeLab, a text-to-analysis framework designed to enhance the reliability of LLM-driven analysis for neural spike data.
• To enforce bounded autonomy in LLMs, prioritizing correctness and methodological rigor over efficiency.
• To enable natural language-based analysis of complex electrophysiology data without manual coding.

Main Methods:

• Development of SpikeLab, integrating composable data structures with a skill-based agentic system.
• Implementation of bounded autonomy principles: mandatory expert-vetted methods, correctness focus, and clarification-seeking.
• Controlled benchmark testing of Sonnet 4.6 with SpikeLab against unassisted Sonnet and Opus 4.6 on electrophysiology data.

Main Results:

• SpikeLab-enhanced Sonnet 4.6 achieved correct and reproducible results across all benchmark tasks.
• Unassisted LLMs (Sonnet and Opus 4.6) exhibited deterministic failures, including ad hoc method invention and silent data reduction.
• The framework demonstrated versatility across diverse recording types (in vivo, human, in vitro) and complex analyses.

Conclusions:

• SpikeLab effectively mitigates risks associated with LLM application in scientific analysis, particularly for neural spike data.
• The framework ensures methodological soundness and reproducibility, outperforming unassisted state-of-the-art LLMs in a controlled benchmark.
• SpikeLab facilitates sophisticated electrophysiology data analysis via natural language, broadening accessibility and accelerating discovery.