Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits

2019 ◽  
Vol 179 ◽  
pp. 104986 ◽  
Author(s):  
Taimoor Tariq ◽  
M. Hashim Satti ◽  
Hamid M. Kamboh ◽  
Maryam Saeed ◽  
Awais M. Kamboh
Keyword(s):  
Unit Activity ◽  
Computationally Efficient ◽  
Spike Detection ◽  
Neural Spike ◽  
Fully Automatic ◽  
Implantable Circuits

scholarly journals Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays

2017 ◽  
Vol 13 (11) ◽  
pp. e1005842 ◽  
Author(s):  
Gonzalo E. Mena ◽  
Lauren E. Grosberg ◽  
Sasidhar Madugula ◽  
Paweł Hottowy ◽  
Alan Litke ◽  
...  
Keyword(s):  
Electrical Stimulus ◽  
Electrode Arrays ◽  
Spike Detection ◽  
Neural Spike ◽  
Stimulus Artifact

scholarly journals Hierarchical Sea-Land Segmentation for Panchromatic Remote Sensing Imagery

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Long Ma ◽  
Nouman Q. Soomro ◽  
Jinjing Shen ◽  
Liang Chen ◽  
Zhihong Mai ◽  
...  
Keyword(s):  
State Of The Art ◽  
Temporal Variations ◽  
Memory Storage ◽  
Computationally Efficient ◽  
High Resolution Data ◽  
Digital Elevation ◽  
Segmentation Approach ◽  
Fully Automatic ◽  
Elevation Model ◽  
Coarse To Fine

Automatic sea-land segmentation is an essential and challenging field for the practical use of panchromatic satellite imagery. Owing to the temporal variations as well as the complex and inconsistent intensity contrast in both land and sea areas, it is difficult to generate an accurate segmentation result by using the conventional thresholding methods. Additionally, the freely available digital elevation model (DEM) also difficultly meets the requirements of high-resolution data for practical usage, because of the low precision and high memory storage costs for the processing systems. In this case, we proposed a fully automatic sea-land segmentation approach for practical use with a hierarchical coarse-to-fine procedure. We compared our method with other state-of-the-art methods with real images under complex backgrounds and conducted quantitative comparisons. The experimental results show that our method outperforms all other methods and proved being computationally efficient.

scholarly journals Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 410
Author(s):  
Gerardo Saggese ◽  
Mattia Tambaro ◽  
Elia A. Vallicelli ◽  
Antonio G. M. Strollo ◽  
Stefano Vassanelli ◽  
...  
Keyword(s):  
Firing Rate ◽  
Energy Operator ◽  
Neuronal Firing ◽  
Capacitive Coupling ◽  
Detection Accuracy ◽  
Noise Power ◽  
Spike Detection ◽  
Low Snr ◽  
Detection Algorithms ◽  
Neural Spike

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

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