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Literature DB >> 6396456

Computer separation of multi-unit neuroelectric data: a review.

E M Schmidt.

Abstract

Mesh：

Year: 1984 PMID： 6396456 DOI： 10.1016/0165-0270(84)90009-8

Source DB: PubMed Journal: J Neurosci Methods ISSN： 0165-0270 Impact factor: 2.390

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13 in total

1. Spike sorting based on multi-class support vector machine with superposition resolution.

Authors: Weidong Ding; Jingqi Yuan
Journal: Med Biol Eng Comput Date: 2007-09-15 Impact factor: 2.602

2. Method for determining individual neuron size in simultaneous single-unit recordings.

Authors: W E Faller; M W Luttges
Journal: Med Biol Eng Comput Date: 1995-03 Impact factor: 2.602

3. Semi-supervised spike sorting using pattern matching and a scaled Mahalanobis distance metric.

Authors: Douglas M Schwarz; Muhammad S A Zilany; Melissa Skevington; Nicholas J Huang; Brian C Flynn; Laurel H Carney
Journal: J Neurosci Methods Date: 2012-02-23 Impact factor: 2.390

Review 4. Continuing progress of spike sorting in the era of big data.

Authors: David Carlson; Lawrence Carin
Journal: Curr Opin Neurobiol Date: 2019-03-08 Impact factor: 6.627

5. Analysis of single-unit firing patterns in multi-unit intrafascicular recordings.

Authors: E V Goodall; K W Horch; T G McNaughton; C M Lybbert
Journal: Med Biol Eng Comput Date: 1993-05 Impact factor: 2.602

6. Single unit components of the hypothalamic multiunit electrical activity associated with the central signal generator that directs the pulsatile secretion of gonadotropic hormones.

Authors: H Cardenas; T Ordög; K T O'Byrne; E Knobil
Journal: Proc Natl Acad Sci U S A Date: 1993-10-15 Impact factor: 11.205

Computer separation of multi-unit neuroelectric data: a review.

1. Spike sorting based on multi-class support vector machine with superposition resolution.

2. Method for determining individual neuron size in simultaneous single-unit recordings.

3. Semi-supervised spike sorting using pattern matching and a scaled Mahalanobis distance metric.

Review 4. Continuing progress of spike sorting in the era of big data.

5. Analysis of single-unit firing patterns in multi-unit intrafascicular recordings.

6. Single unit components of the hypothalamic multiunit electrical activity associated with the central signal generator that directs the pulsatile secretion of gonadotropic hormones.

7. Spike sorting of muscle spindle afferent nerve activity recorded with thin-film intrafascicular electrodes.

8. Automatic spike sorting for high-density microelectrode arrays.

9. A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

10. Spike sorting by joint probabilistic modeling of neural spike trains and waveforms.