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The application of closed-loop approaches in systems neuroscience andtherapeutic stimulation holds great promise for revolutionizing ourunderstanding of the brain and for developing novel neuromodulation therapiesto restore lost functions. Neural prostheses capable of multi-channel neuralrecording, on-site signal processing, rapid symptom detection, and closed-loopstimulation are critical to enabling such novel treatments. However, theexisting closed-loop neuromodulation devices are too simplistic and lacksufficient on-chip processing and intelligence. In this paper, we first discussboth commercial and investigational closed-loop neuromodulation devices forbrain disorders. Next, we review state-of-the-art neural prostheses withon-chip machine learning, focusing on application-specific integrated circuits(ASIC). System requirements, performance and hardware comparisons, designtrade-offs, and hardware optimization techniques are discussed. To facilitate afair comparison and guide design choices among various on-chip classifiers, wepropose a new energy-area (E-A) efficiency figure of merit that evaluateshardware efficiency and multi-channel scalability. Finally, we present severaltechniques to improve the key design metrics of tree-based on-chip classifiers,both in the context of ensemble methods and oblique structures.

Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection