Journal ArticleDOI
Unsupervised automatic online spike sorting using reward-based online clustering
TLDR
A novel method for clustering referred to as Reward-Based Online Clustering (RBOC) which is formed based on the reinforcement learning algorithm and can automatically detect the clusters while there is no knowledge about the number of clusters.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2020-02-01. It has received 10 citations till now. The article focuses on the topics: Spike sorting & Sorting.read more
Citations
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Journal ArticleDOI
Facilitating stochastic resonance as a pre-emphasis method for neural spike detection
Cihan Berk Gungor,Hakan Toreyin +1 more
TL;DR: The proposed SR-based spike detection improves the signal-to-noise ratio of the intracellular-based synthetic dataset as much as 7.35 dB and outperforms the state-of-the-art pre-emphasis methods in false positive and false negative rates in 15 of the 16 synthetic extracellular datasets.
Journal ArticleDOI
From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings
TL;DR: This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community.
Journal ArticleDOI
Investigating well potential parameters on neural spike enhancement in a stochastic-resonance pre-emphasis algorithm
TL;DR: In this paper, the authors investigated how the well shape and damping status impact the output signal-to-noise ratio (SNR) and compared the overdamped and underdamped solutions of shallow- and steep-wall monostable wells and bistable wells in terms of SNR improvement using two synthetic datasets.
Journal ArticleDOI
Nearly symmetric orthogonal wavelets for time-frequency-shape joint analysis: Introducing the discrete shapelet transform’s third generation (DST-III) for nonlinear signal analysis
TL;DR: The Discrete Shapelet Transform (DST-III) as discussed by the authors is the third generation of the original DST-I and II, which is used for time-frequency shape (TFS) joint analysis.
Journal ArticleDOI
An optimized GMM algorithm and its application in single-trial motor imagination recognition
Rongrong Fu,Zheyu Li,Juan Wang +2 more
TL;DR: In this paper, an optimized Gaussian mixture model (GMM) clustering technique that exhibits low sensitivity with respect to outliers within clusters has been proposed, which eliminates deviations caused by outliers.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Posted Content
Reinforcement Learning: A Survey
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Journal ArticleDOI
Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
TL;DR: A new method for detecting and sorting spikes from multiunit recordings that combines the wave let transform with super paramagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions is introduced.