Multichannel Many-Class Real-Time Neural Spike Sorting With Convolutional Neural Networks
Author:
Affiliation:
1. Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA, US
Funder
National Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Medicine
Link
https://ieeexplore.ieee.org/ielam/8784029/9684754/9896230-aam.pdf
Reference34 articles.
1. High Density, Double-Sided, Flexible Optoelectronic Neural Probes With Embedded μLEDs
2. A Fully Automated Approach to Spike Sorting
3. Technology-Aware Algorithm Design for Neural Spike Detection, Feature Extraction, and Dimensionality Reduction
4. Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm
5. Visualizing Time Series Data with Temporal Matching Based t-SNE
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1. Computationally Efficient Unsupervised Online Spatial Spike Sorting Algorithm;IEEE Access;2024
2. A 16-channel Reconfigurable Neural Spike Sorting System with Distributed Tiles and Neural Network Engine Based on FPGA;2023 IEEE Biomedical Circuits and Systems Conference (BioCAS);2023-10-19
3. A Deep Neural Network-Based Spike Sorting With Improved Channel Selection and Artefact Removal;IEEE Access;2023
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