Application of a maximum likelihood algorithm to ultrasound modulated optical tomography

2011 ◽  
Author(s):  
Nam Trung Huynh ◽  
Diwei He ◽  
Barrie R. Hayes-Gill ◽  
John A. Crowe ◽  
John G. Walker ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Optical Tomography ◽  
Maximum Likelihood Algorithm

scholarly journals Application of a maximum likelihood algorithm to ultrasound modulated optical tomography

2012 ◽  
Vol 17 (2) ◽  
pp. 026014 ◽  
Author(s):  
Nam T. Huynh ◽  
Diwei He ◽  
Barrie R. Hayes-Gill ◽  
John A. Crowe ◽  
John G. Walker ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Optical Tomography ◽  
Maximum Likelihood Algorithm

A Maximum Likelihood Algorithm for Estimating Damped Sinusoids

1990 ◽  
Vol 23 (1) ◽  
pp. 601-605
Author(s):  
D.R. Farrier ◽  
A.R. Prior-Wandesforde
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Algorithm

scholarly journals DIRECTION FINDING FOR BISTATIC MIMO RADAR USING EM MAXIMUM LIKELIHOOD ALGORITHM

2013 ◽  
Vol 141 ◽  
pp. 99-116 ◽  
Author(s):  
Hao Wen Chen ◽  
Degui Yang ◽  
Hong-Qiang Wang ◽  
Xiang Li ◽  
Zhaowen Zhuang
Keyword(s):  
Maximum Likelihood ◽  
Direction Finding ◽  
Mimo Radar ◽  
Maximum Likelihood Algorithm ◽  
Bistatic Mimo Radar

scholarly journals Automatic Spike Sorting Using Tuning Information

2009 ◽  
Vol 21 (9) ◽  
pp. 2466-2501 ◽  
Author(s):  
Valérie Ventura
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Poisson Processes ◽  
Spike Sorting ◽  
Maximum Likelihood Algorithm ◽  
Firing Rates ◽  
Novel Approach ◽  
Misclassification Rates ◽  
Available Information ◽  
Spike Waveforms

Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

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