skip to main content


Title: Multichannel Many-Class Real-Time Neural Spike Sorting With Convolutional Neural Networks
Award ID(s):
2028893 1953801 1952907
NSF-PAR ID:
10373435
Author(s) / Creator(s):
 ;  ;  ;  ;  
Publisher / Repository:
Institute of Electrical and Electronics Engineers
Date Published:
Journal Name:
IEEE Open Journal of Circuits and Systems
Volume:
3
ISSN:
2644-1225
Format(s):
Medium: X Size: p. 168-179
Size(s):
["p. 168-179"]
Sponsoring Org:
National Science Foundation
More Like this
  1. This paper aims to enhance the computational efficiency of safety verification of neural network control systems by developing a guaranteed neural network model reduction method. First, a concept of model reduction precision is proposed to describe the guaranteed distance between the outputs of a neural network and its reduced-size version. A reachability-based algorithm is proposed to accurately compute the model reduction precision. Then, by substituting a reduced-size neural network controller into the closed-loop system, an algorithm to compute the reachable set of the original system is developed, which is able to support much more computationally efficient safety verification processes. Finally, the developed methods are applied to a case study of the Adaptive Cruise Control system with a neural network controller, which is shown to significantly reduce the computational time of safety verification and thus validate the effectiveness of the method. 
    more » « less
  2. In this paper, we propose a concept of approximate bisimulation relation for feedforward neural networks. In the framework of approximate bisimulation relation, a novel neural network merging method is developed to compute the approximate bisimulation error between two neural networks based on reachability analysis of neural networks. The developed method is able to quantitatively measure the distance between the outputs of two neural networks with the same inputs. Then, we apply the approximate bisimulation relation results to perform neural networks model reduction and compute the compression precision, i.e., assured neural networks compression. At last, using the assured neural network compression, we accelerate the verification processes of ACAS Xu neural networks to illustrate the effectiveness and advantages of our proposed approximate bisimulation approach. 
    more » « less