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This paper reports a novel neurotechnology (Neuronix) and its validation through experiments. It is a miniature system-on-chip (SoC) that allows recording with simultaneous electrical microstimulation. This function has not been demonstrated before and enables precise, closed-loop neuromodulation. Neuronix represents recent advancement in brain technology and applies to both animal research and clinical...
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection...
This paper focuses on biomimetic systems for building advanced neuroscience and neuroprosthetics platform with closed-loop control mechanisms. Works on neural implants consist of four major functional blocks of stimulation, recording, processing, and wireless communication, each of which has different challenges to be overcome. On-chip integration of these fundamental blocks with low power requirements...
This paper presents a novel nonparametric clustering algorithm, called energy based evolving mean shift (EMS) clustering. It defines an energy function to characterize the compactness of the underlying data set and proves the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. Although as a general algorithm,...
An emerging class of multi-channel neural recording systems aims to simultaneously monitor the activity of many neurons by miniaturizing and increasing the number of recording channels. Vast volume of data from the recording systems, however, presents a challenge for processing and transmitting wirelessly. An on-chip neural signal processor is needed for filtering uninterested recording samples and...
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