  The application of closed-loop approaches in systems neuroscience and
therapeutic stimulation holds great promise for revolutionizing our
understanding of the brain and for developing novel neuromodulation therapies
to restore lost functions. Neural prostheses capable of multi-channel neural
recording, on-site signal processing, rapid symptom detection, and closed-loop
stimulation are critical to enabling such novel treatments. However, the
existing closed-loop neuromodulation devices are too simplistic and lack
sufficient on-chip processing and intelligence. In this paper, we first discuss
both commercial and investigational closed-loop neuromodulation devices for
brain disorders. Next, we review state-of-the-art neural prostheses with
on-chip machine learning, focusing on application-specific integrated circuits
(ASIC). System requirements, performance and hardware comparisons, design
trade-offs, and hardware optimization techniques are discussed. To facilitate a
fair comparison and guide design choices among various on-chip classifiers, we
propose a new energy-area (E-A) efficiency figure of merit that evaluates
hardware efficiency and multi-channel scalability. Finally, we present several
techniques to improve the key design metrics of tree-based on-chip classifiers,
both in the context of ensemble methods and oblique structures.
