The detection of transient responses, i.e. nonstationarities, that
arise in a varying and small fraction of the total number of neural
spike trains recorded from chronically implanted multielectrode grids
becomes increasingly difficult as the number of electrodes grows.
This paper presents a novel application of an unsupervised neural
network for clustering neural spike trains with transient responses.
This network is constructed by incorporating projective clustering
into an adaptive resonance type neural network (ART) architecture
resulting in a PART neural network. Since comparisons are made between
inputs and learned patterns using only a subset of the total number
of available dimensions, PART neural networks are ideally suited
to the detection of transients. We show that PART neural networks
are an effective tool for clustering neural spike trains that is
easily implemented, computationally inexpensive, and well suited
for detecting neural responses to dynamic environmental stimuli.
%0 Generic
%1 Hunter2008
%A Hunter, John D.
%A Wu, Jianhong
%A Milton, John G.
%B Decision and Control, 2008. CDC 2008. 47th IEEE Conference on
%D 2008
%J IEEE
%K Neuronale modelle
%T Clustering Neural Spike Trains with Transient Responses
%X The detection of transient responses, i.e. nonstationarities, that
arise in a varying and small fraction of the total number of neural
spike trains recorded from chronically implanted multielectrode grids
becomes increasingly difficult as the number of electrodes grows.
This paper presents a novel application of an unsupervised neural
network for clustering neural spike trains with transient responses.
This network is constructed by incorporating projective clustering
into an adaptive resonance type neural network (ART) architecture
resulting in a PART neural network. Since comparisons are made between
inputs and learned patterns using only a subset of the total number
of available dimensions, PART neural networks are ideally suited
to the detection of transients. We show that PART neural networks
are an effective tool for clustering neural spike trains that is
easily implemented, computationally inexpensive, and well suited
for detecting neural responses to dynamic environmental stimuli.
@conference{Hunter2008,
abstract = {The detection of transient responses, i.e. nonstationarities, that
arise in a varying and small fraction of the total number of neural
spike trains recorded from chronically implanted multielectrode grids
becomes increasingly difficult as the number of electrodes grows.
This paper presents a novel application of an unsupervised neural
network for clustering neural spike trains with transient responses.
This network is constructed by incorporating projective clustering
into an adaptive resonance type neural network (ART) architecture
resulting in a PART neural network. Since comparisons are made between
inputs and learned patterns using only a subset of the total number
of available dimensions, PART neural networks are ideally suited
to the detection of transients. We show that PART neural networks
are an effective tool for clustering neural spike trains that is
easily implemented, computationally inexpensive, and well suited
for detecting neural responses to dynamic environmental stimuli.},
added-at = {2012-01-27T14:10:42.000+0100},
author = {Hunter, John D. and Wu, Jianhong and Milton, John G.},
biburl = {https://www.bibsonomy.org/bibtex/201621a655a958d9e48fb4d231a50b5f1/muhe},
booktitle = {Decision and Control, 2008. CDC 2008. 47th IEEE Conference on},
file = {Clustering Neural Spike Trains with Transient Responses.pdf:2008\\Clustering Neural Spike Trains with Transient Responses.pdf:PDF},
interhash = {0e5d4ab295cb5a0209f14f5de951b9c7},
intrahash = {01621a655a958d9e48fb4d231a50b5f1},
journal = {IEEE},
keywords = {Neuronale modelle},
owner = {Mu},
pdf = {2008\Clustering Neural Spike Trains with Transient Responses.pdf},
timestamp = {2012-01-27T14:10:54.000+0100},
title = {Clustering Neural Spike Trains with Transient Responses},
year = 2008
}