Download A Comparison of Neural Spike Classification Techniques.

A Comparison of Neural Spike Classification Techniques.
J. P. Stitt1, R. P. Gaumond1, J. L. Frazier1, and F. E. Hanson2
1 Pennsylvania State University,
2 University of Maryland at Baltimore
INTRODUCTION
A number of techniques have been employed to
classify spike shape. These spikes can be classified by
characteristics such as amplitude and temporal features. Two
of the classical optimal methods applied to neural spike
classification are Template Matching (TM) and Principal
Component analysis (PC) [1]. In this paper we compare the
performance of an Artificial Neural Network (ANN) with the
performance of the two well known methods of spike
classification. The classifier that provides the best
performance will be incorporated into a system that is capable
of predicting behavior when provided with an estimate of the
activity levels of eight sensory neurons.
METHODS
Electrophysiological recordings are obtained from
the two taste organs (the Lateral and Medial Sensilla) that
provide the primary sensory input to the feeding behavior
center of a caterpillar, the larval Manduca Sexta (M. Sexta).
Each of the taste organs consists of four chemosensory
neurons. The eight neurons respond to distinct classes of
chemical compounds and together provide the CNS with a
chemical analysis of the substance that is about to be eaten.
The resulting chemical analysis is transmitted to the CNS
along eight parallel pathways. The activity level of an
individual neuron is conveyed as a pulse frequency modulated
(PFM) signal were spike frequency is modulated by
chemoreceptor activity level. Since any or all of the
constituent neurons of a sensillum may be activate, each
multiunit recording can consist of a superposition of up to
four single-unit spike trains. To estimate the state of the taste
organ we must estimate the level of the constituent neurons'
activity levels from their PFM signals. We do this by
classifying the spikes of a multiunit recording (typically one
second) and counting the numbers of each spike type.
There are specific chemical compounds (termed
reference compounds) that are known to illicit activity form
only one of the four neurons in each sensillum. The neuron
that is referenced by a specific compound is labeled with the
compound's chemical name (i.e., Inositol; Glucose; Canna;
and KCl are the four reference compounds associated with the
medial sensillum). Spikes extracted from these single-unit
recordings can be used to determine the unique spike shape
that is produced by each of the four neurons.
The insect samples the leaf upon which it is feeding
approximately once per second. Thus we record one second of
neural activity after the application of a chemical stimulus.
These one-second trials can be subdivided into two temporal
regions; the phasic (transient) response, a n d t h e tonic
(steady-state) response. During the first 150 milliseconds of a
recording the pulse amplitude and spike frequency steadily
increase. This is the phasic region on the response. Within
the tonic region (i.e., remaining 850 milliseconds) the pulse
amplitude and spike frequency remain fairly constant. The
response of the Medial Inositol neuron displays a clear phasic
and tonic response.
The one-second trials are recorded and digitized.
Next, a detection algorithm is applied to the sequence of
sampled amplitudes. When a spike is detected, its amplitude
samples are extracted and stored as a column vector in a
matrix where columns contain all of the extracted spikes, in
chronological order, from a given trial.
The sampling rate was 10K Hz and the first 3.2
milliseconds (i.e., 32 Samples) of a spike's waveform were
used to classify the spike's origin. Multiple trials of the four
reference compounds were presented to the same sensillum of
the same animal. The individual spikes from all trials of a
specific reference compound form the spike ensemble of the
corresponding neuron. The result was four ensembles
representing the four classes of spikes for a specific sensillum.
Inositol
Amplitude; (mV)
ABSTRACT
This paper presents an Artificial Neural Network (ANN)
capable of sorting neural spikes contained in a single-channel
multiunit recording. The ANN performs very well when
compared with Template Matching and Principal
Components, two of the conventional optimal spike
classification methods that have been widely used for sorting
action potentials.
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Canna
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KCl
Glucose
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20
30
Sample Number
Figure 1: Medial Ensemble Averages
Two types of averaged spikes were used to form the
classifiers in the following discussion. The prototype spike
was formed by averaging together all the individuals extracted
from one trial, while the exemplar spike was formed by
averaging all of the prototype spikes of a given reference
specific reference compound were produced by the referenced
neuron.
The ANN classifier correctly classified 96% of the
pikes that occurred during the phasic portion of the Inositol
trials while PC and TM correctly classified 87%. On phasic
Canna spikes the ANN classified 80% correctly with both the
PC and TM methods correctly classifying 56%. When
classifying phasic Glucose spikes the TM and PC methods
both classified 92% correctly and the ANN correctly classified
86%. All of the classifiers performed very well when
presented the phasic KCl spikes.
All three methods produced perfect results for tonic
Inositol spikes. The ANN classified 99% of the tonic Glucose
spikes correctly, TM classified 91% correctly, and the PC
method classified 88% correctly. The tonic Canna spikes
were correctly classified 95% by the ANN with the PC
classifier producing 89% correct and the TM technique
classifying 88% correctly. Again, all three classifiers produced
nearly perfect results for tonic KCl spikes.
DISCUSSION
The phasic Inositol spikes show the greatest change
with pulse amplitude increasing by 30%. Thus the Inositol
spikes have the greatest amount of variability in amplitude
distributions. Figure 2 shows several Inositol spikes that
occurred early in the phasic response (dark gray);
superimposed over the four medial ensemble averages (light
gray). The value of the peak amplitude of these early phasic
Inositol spikes resembles the Glucose exemplar more than
they resemble the Inositol exemplar and this leads to
misclassification. The amplitude distributions of Glucose and
Canna showed the greatest amount of overlap and are thus the
most challenging classification problem. Tonic Inositol
spikes and all KCl spikes had the least overlap with other
distributions and present little difficulty to the classifiers.
0.5
Amplitude; (mV)
compound. The exemplar spike is also the ensemble average.
Figure 1 shows a plot of the four exemplars that are
associated with the Medial Sensillum.
The first classification technique to be discussed is
template matching [1,2]. Each class is associated with a
template vector. In our case the templates are identical to the
ensemble averages. The template method computes the root
of the squared difference between an individual spike and the
four templates. The template that results in the smallest value
serves as the class of the individual spike.
The second spike classification technique is the
method of principal components [ 1 ] . T h e P C a r e t h e
orthonormal basis vectors of the composite covariance matrix
of the four exemplar spikes. An individual covariance matrix
is formed by computing the outer product of each exemplar
spike vector with its transpose. The four individual
covariance matrices are summed to form the composite
covariance matrix. The composite covariance matrix is
orthogonalized, resulting in a orthogonal matrix whose
columns are the eigenvectors (orthonormal basis) of the
composite covariance matrix. The eigenvectors serve as the
PCs, and we use the two eigenvectors associated with the
two largest eigenvalues. Two features are calculated for each
individual spike by evaluating the inner product of the spike
with the two PCs. The two features associated with an
individual spike and each of the four exemplars can be viewed
as the coordinates of points on a Cartesian plane. The
individual spike is assigned to the class whose exemplar
features are closest to it (i.e., minimum Euclidean distance).
The final spike sorting method employs an ANN
classifier. We developed a two-layer feedforward ANN and
trained it with the error back-propagation algorithm [3]. The
ANN consists of 32 input nodes, one node to store each
sample value of a spike vector. Each input node is fully
connected to 3 hidden-layer artificial neurons or processing
elements (PE) and each hidden-layer PE is fully connected to
four output-layer PEs. Each output-layer PE is associated
with one of the sensillum's reference neurons. The range of
each output-layer neuron is a value between [-1,1] and
corresponds to the likelihood that the spike present at the
ANN's input belongs to the associated reference neuron's
class. The ANN was trained until the value of the sumsquared error (SSE) between desired outputs and the actual
ANN outputs fell below acceptable level. The three hiddenlayer PEs resulted in an ANN that could be consistently
trained to the acceptable level of SSE within a minimal time.
The ANN's training set was formed by the prototype spikes
and the desired output for each prototype spike. In addition to
the prototype spikes several individual phasic Inositol spikes
were included to improve classification results.
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RESULTS
The three classifiers were applied to all of the
individual spikes of the four ensembles. The classification
results for phasic and tonic regions are discussed separately.
All of the spikes that were extracted from any trial were
visually examined and any noise waveforms were eliminated.
It is assumed that all spikes that occur during the trial of a
10
20
30
Sample Number
Figure 2: Early phasic region Inositol spikes
(dark gray) superimposed over the medial
ensemble average spikes (light gray).
The ANN performed better in dealing with the
highly overlapping Glucose and Canna distributions showing
a total (phasic and tonic) misclassification of 4% while the
template method resulted in 8% error and the PC method
misclassified 9%. The ANN training set consists of multiple
spikes rather than one average spike. This permits the ANN
to learn amplitude and shape distributions rather than
averages. Both the PC and template methods depend upon
Gaussian distributions while the ANN can handle a greater
variety of distributions.
The ANN classifier out performed both of the classic
methods when classifying phasic Inositol spikes. The ANN
was originally trained with prototype spikes and resulted in
an error rate greater than the PC or template methods. The
ANN's original training set was augmented by adding several
phasic Inositol spikes extracted early in their spike trains.
This addition resulted in a significantly improved classifier
(from 30% error to 4%). The flexibility of being able to
modify the ANN's training set, thus permitting it to learn
special cases is another advantage to the ANN type of
classifier.
A significant disadvantage to the ANN method is
the time required to determine optimal values for training
parameters. Optimal parameter values are necessary to
minimize the overall training time and SSE whereas both the
PC and template classifiers are determined by a one pass
operation.
REFERENCES
[1] Wheeler, B. C., 1996. Multiple Unit Neural Spike
Sorting, manuscript submitted for publication in , Neural
Engineering, Y. I. Kim and N. Thakor, editors.
[2] Frazier, J. L. and F. E. Hanson, 1986,
Electrophysiological Recordings and Analysis of Insect
Chemosensory Responses, in Insect/Plant Interactions, J. R.
Miller and T. A. Miller (editors), New York:Springer-Verlag.
[3] Zurada, J. M., 1992. Introduction to Artificial Neural
Systems, St. Paul:West.