Background noise and spike overlap pose problems in contemporary spike-sorting strategies. In this paper, both issues are addressed by a hybrid scheme that combines the robust representation of spike waveforms to facilitate the reliable identification of contributing neurons with efficient data learning to enable the precise decomposition of coactivations.
Exoskeleton Retinal prosthesis human to human interface Connectomics Graph Theory neuroethics tCS CPG Biometrics Plasticity Trajectories systems-neuroscience music recommendation music neuroscience Deep Learning Interpretability Learnable Filters network neuroscience multiplexing Conferences music perception brain networks modularity community detection brain to brain interface transcranial direct current stimulation music Sparse neurons Noise Deep Brain Stimulation LFP Neural coding TMS Computational Neuroscience Brain-inspired Computing Neuroimaging Wavelets Neuro-inspired Computing neuroprosthetics Bionics Machine Learning neural recordings EEG Information Theory Dimensionality Reduction Modelling Neural Networks Clustering Statistical Analysis Neurophysiology Spike Sorting Brain Research Brain Interfaces
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