An Adaptive Neural Spike Processor With Embedded Active Learning for Improved Unsupervised Sorting Accuracy
نویسندگان
چکیده
منابع مشابه
A 75-µW, 16-Channel Neural Spike-Sorting Processor With Unsupervised Clustering
Abstract We describe a neural spike-sorting processor that provides unsupervised clustering simultaneously for 16 channels. The use of a two-stage clustering algorithm, noise-tolerant distance metric, and selectively clocked high-VT register arrays makes online clustering feasible for implementation. The spike-sorting processor has a power consumption of 75μW at 270mV and an area of 2.45mm in a...
متن کاملComparison of Adaptive Voltage/Frequency Scaling and Asynchronous Processor Architectures for Neural Spike Sorting
This paper investigates the tradeoffs between adaptive and asynchronous timing methods for spike sorting in neural signal processing. Using an asynchronous timing scheme has been proven to reduce the power consumed by a neural processor [1], yet no relevant comparison has been made with an adaptive timing scheme. Our work provides an evaluation of the power consumption of two spike-sorting circ...
متن کاملHigh-Dimensional Unsupervised Active Learning Method
In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...
متن کاملUnsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering
This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds ...
متن کاملHierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.
This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike da...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Biomedical Circuits and Systems
سال: 2018
ISSN: 1932-4545,1940-9990
DOI: 10.1109/tbcas.2018.2825421