Artificial neural network for neural spike classification

J. P. Stitt; R. P. Gaumond; J. L. Frazier; F. E. Hanson

Artificial neural network for neural spike classification

J. P. Stitt
, R. P. Gaumond
, J. L. Frazier
, F. E. Hanson

Materials Characterization Lab

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

We discuss an Artificial Neural Network capable of sorting neural spikes contained in a mixed spike train. The ANN performs very well when compared with conventional optimal methods of Template Matching and Principal Components.

Original language	English (US)
Pages	15-16
Number of pages	2
State	Published - 1997
Event	Proceedings of the 1997 23rd Annual Northeast Bioengineering Conference - Durham, NH, USA Duration: May 21 1997 → May 23 1997

Other

Other	Proceedings of the 1997 23rd Annual Northeast Bioengineering Conference
City	Durham, NH, USA
Period	5/21/97 → 5/23/97

All Science Journal Classification (ASJC) codes

Bioengineering

Cite this

@conference{618ca8e24ba847588c172395d30b8bef,

title = "Artificial neural network for neural spike classification",

abstract = "We discuss an Artificial Neural Network capable of sorting neural spikes contained in a mixed spike train. The ANN performs very well when compared with conventional optimal methods of Template Matching and Principal Components.",

author = "Stitt, \{J. P.\} and Gaumond, \{R. P.\} and Frazier, \{J. L.\} and Hanson, \{F. E.\}",

year = "1997",

language = "English (US)",

pages = "15--16",

note = "Proceedings of the 1997 23rd Annual Northeast Bioengineering Conference ; Conference date: 21-05-1997 Through 23-05-1997",

}

TY - CONF

T1 - Artificial neural network for neural spike classification

AU - Stitt, J. P.

AU - Gaumond, R. P.

AU - Frazier, J. L.

AU - Hanson, F. E.

PY - 1997

Y1 - 1997

N2 - We discuss an Artificial Neural Network capable of sorting neural spikes contained in a mixed spike train. The ANN performs very well when compared with conventional optimal methods of Template Matching and Principal Components.

AB - We discuss an Artificial Neural Network capable of sorting neural spikes contained in a mixed spike train. The ANN performs very well when compared with conventional optimal methods of Template Matching and Principal Components.

UR - https://www.scopus.com/pages/publications/0030647954

UR - https://www.scopus.com/pages/publications/0030647954#tab=citedBy

M3 - Paper

AN - SCOPUS:0030647954

SP - 15

EP - 16

T2 - Proceedings of the 1997 23rd Annual Northeast Bioengineering Conference

Y2 - 21 May 1997 through 23 May 1997

ER -

Artificial neural network for neural spike classification

Abstract

Other

All Science Journal Classification (ASJC) codes

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