Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Closed-Loop Neural Interfaces with Embedded Machine Learning   Neural interfaces capable of multi-site electrical recording, on-site signal
classification, and closed-loop therapy are critical for the diagnosis and
treatment of neurological disorders. However, deploying machine learning
algorithms on low-power neural devices is challenging, given the tight
constraints on computational and memory resources for such devices. In this
paper, we review the recent developments in embedding machine learning in
neural interfaces, with a focus on design trade-offs and hardware efficiency.
We also present our optimized tree-based model for low-power and
memory-efficient classification of neural signal in brain implants. Using
energy-aware learning and model compression, we show that the proposed oblique
trees can outperform conventional machine learning models in applications such
as seizure or tremor detection and motor decoding.

13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph