Sequential analysis and its applications to neuromorphic engineering

Area of Science:

• Neuromorphic Engineering
• Computational Neuroscience
• Statistical Analysis

Background:

• Neuromorphic circuits rely on threshold-based signal comparison for sensing and computation.
• Rigorous analysis is hindered by inaccessible statistical tools within the neuromorphic community.

Purpose of the Study:

• Introduce sequential analysis as a tool for threshold-crossing problems in neuromorphic engineering.
• Demonstrate its application as a benchmark, proxy model, and design tool.

Main Methods:

• Present Abraham Wald's sequential analysis methodology.
• Apply the framework to three neuromorphic engineering examples.
• Ensure accessibility without prior probability or statistics training.

Main Results:

• Sequential analysis establishes statistical performance limits for neuromorphic circuits.
• It provides tractable abstractions of complex circuit dynamics.
• Offers constructive design rules and optimal threshold determination.

Conclusions:

• Sequential analysis provides rigorous statistical baselines for hardware evaluation.
• Clarifies the computational role of neuromorphic architectures by linking circuit parameters to dynamics.
• Enables principled circuit design beyond empirical tuning.