https://github.com/alexdelitzas/fcdae-neural-signal-denoising
공식 코드Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aripakman/neural_clustering_process
공식 코드https://github.com/colehurwitz/vae_spike_localization
공식 코드Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
https://github.com/ds2p/crsae
공식 코드Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
https://github.com/klusta-team/klustakwik
공식 코드High-dimensional cluster analysis with the Masked EM Algorithm
https://github.com/lucashancef/e-sort
공식 코드https://github.com/lucashancef/fafesort
공식 코드https://github.com/qingqu06/sparse_deconvolution
공식 코드https://github.com/tensorflow/neural-structured-learning/tree/master/research/neural_clustering
공식 코드https://github.com/timothyzh/mwscas2022spikesortingcriteria
공식 코드Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
https://github.com/tong-wu-umn/spike-compression-autoencoder
공식 코드Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording
https://github.com/aripakman/amortized_community_detection
https://github.com/aripakman/attentive_clustering_processes
https://github.com/yueqiw/ncp-sort
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.