Investigating the effect of Cortical Discharge Variability on the accuracy of population decoders
BMC Neuroscience ,
Jul 2008
Mehdi Aghagolzadeh , Karim Oweiss
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Investigating the effect of Cortical Discharge Variability on the accuracy of population decoders
BMC Neuroscience
Poster presentation Investigating the effect of Cortical Discharge Variability on the accuracy of population decoders
Mehdi Aghagolzadeh 1
Karim Oweiss 0 1
0 Neuroscience Program, Michigan State University , Michigan, 48824 , USA
1 Department of Electrical and Computer Engineering, Michigan State University , Michigan, 48824 , USA
utilized to model the conditional probability of the intended movement based on the observed response
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Estimation of the response properties of cortical neurons
from within a recorded population is an essential
component in a cortically-controlled brain machine interface
application. The response properties of interest typically
include precise spike timing, mean firing rate and any
inherent correlation in the activity of the recorded
ensemble. These response properties are essential for the
operation of a neural decoder that translates the observed
cortical activity to command signals for controlling
robotic arms. In this work we investigate the effect of
cortical discharge variability on the decoding performance.
Specifically cortical discharge variability was induced in
two types of cortical network models. The first one is a
probabilistic model in which the activity was modeled as
an inhomogeneous Poisson process with firing
probability that depends on the neuron's own firing history and
those of other neurons connected to it through
time-varying synaptic couplings. The second is a biophysical leaky
integrate and fire model. Neurons in both models were
cosine-tuned to movement direction with random tuning
widths.
We examined the performance of three types of decoders
to response variability expressed in terms of variations in
the size of the observable neural population used for
decoding and also in terms of the contaminating network
noise. These variations may be indicative of recording
stability in any given experiment. The first is a maximum
likelihood decoder, in which the probability of the
intended movement given the response is estimated
through Bayes rule. Independent Gaussian models were
1 e
2ps i2
rFAeiccgcouurrdaeecd1y opofpduifflaetrieonnt Ndecoders versus the size of the
Accuracy of different decoders versus the size of the
recorded population N. A total of 45 neurons were involved
in encoding the simulated movement at any given
experiments. Only N of these were used for decoding.
where ri is the number of spikes for the ith neuron over a
fixed bin width. The mean and variance of each Gaussian
model is defined through the training process. The other
two decoders are the Wiener and the Kalman filters. The
entire population was 45 neurons and we used a random
subset of these for decoding. The maximum likelihood
decoder demonstrates a superior performance compared
to the Weiner and Kalman based decoders (Fig. 1).
(...truncated)
This is a preview of a remote PDF: http://www.biomedcentral.com/content/pdf/1471-2202-9-S1-P2.pdf
Mehdi Aghagolzadeh, Karim Oweiss.
Investigating the effect of Cortical Discharge Variability on the accuracy of population decoders ,
BMC Neuroscience,
2008, pp. P2, 9,