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Spike Sorting
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Spike sorting is a class of techniques used in the analysis of electrophysiological data. Spike sorting algorithms use the shape(s) of waveforms collected with one or more electrodes in the brain to distinguish the activity of one or more neurons from background electrical noise.
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A Real-time and Hardware Efficient Artfecat-free Spike Sorting Using Deep Spike Detection
SpikeSift: A Computationally Efficient and Drift-Resilient Spike Sorting Algorithm
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes
Pulse Processing -- Overview and Challenges
SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation
L-Sort: On-chip Spike Sorting with Efficient Median-of-Median Detection and Localization-based Clustering
Inferring collective synchrony observing spiking of one or several neurons
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing
68-Channel Highly-Integrated Neural Signal Processing PSoC with On-Chip Feature Extraction, Compression, and Hardware Accelerators for Neuroprosthetics in 22nm FDSOI
L-Sort: An Efficient Hardware for Real-time Multi-channel Spike Sorting with Localization
Neuromorphic Online Clustering and Classification
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting
Zydeco-Style Spike Sorting Low Power VLSI Architecture for IoT BCI Implants
An Adaptive Contrastive Learning Model for Spike Sorting
Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting
Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking
SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm
Comparison of Different Spike Sorting Subtechniques Based on Rat Brain Basolateral Amygdala Neuronal Activity
Improving the Izhikevich Model Based on Rat Basolateral Amygdala and Hippocampus Neurons, and Recognizing Their Possible Firing Patterns
Short-and-Sparse Deconvolution -- A Geometric Approach
Fast Convolutional Dictionary Learning off the Grid
Large scale Lasso with windowed active set for convolutional spike sorting
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Neural Clustering Processes
Evaluation of spike sorting algorithms: Simulations and application to human Subthalamic Nucleus recordings
Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
Power efficient Spiking Neural Network Classifier based on memristive crossbar network for spike sorting application
Comparison of feature extraction and dimensionality reduction methods for single channel extracellular spike sorting
Validation of neural spike sorting algorithms without ground-truth information
Deconvolution of High-Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain
Unsupervised Spike Sorting Based on Discriminative Subspace Learning
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