Furche, Julia

Juárez Paz, León M

Kneib, Thomas

Kretzberg, Jutta

Köpcke, Lena S

English

PubMed

Juárez Paz León M

Furche Julia

Köpcke Lena S

Kneib Thomas

Kretzberg Jutta

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BMC Neuroscience

Comparison of a Bayesian and a regression model for stimulus classification

Lena S Köpcke

Julia Furche

León M Juárez Paz

Thomas Kneib

Jutta Kretzberg

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POSTER PRESENTATION Open Access
Comparison of a Bayesian and a regression
model for stimulus classification
Lena S Köpcke1,2*, Julia Furche1, León M Juárez Paz2, Thomas Kneib1, Jutta Kretzberg2
From Twentieth Annual Computational Neuroscience Meeting: CNS*2011
Stockholm, Sweden. 23-28 July 2011
The central nervous system relies on spikes of retinal
ganglion cells (RGC) as the only source of information
about the visual environment. Therefore, the RGC
response must contain information about e.g. velocity
and luminance of an object. A common approach to
find out which spike train features encode different sti-
mulus properties is to estimate stimuli by assigning the
observed spike trains to stimulus classes according to
their features [1]. Here, we compare two statistical
methods – a Bayesian and a categorical regression
model – for stimulus estimation.
Bayesian stimulus estimation has been used in neuros-
cientific literature for many years [2]. Its basic idea is to
determine the most probable stimulus given the
observed response by using prior knowledge about the
probability of stimulus occurrence combined with the
statistics of response properties, conditioned on the sti-
mulus. The second approach, a regression model based
on categorical ordered data, is new to the field of neuro-
nal stimulus estimation. The so called bivariate cumula-
tive probit model [3] assumes that the bivariate ordinal
response variable (e.g. the combination of ordered light
intensity and velocity) is a discretization of a continuous
variable that cannot be observed directly but can be
described by a standard linear model with spike train
features as covariates.
We analyzed multi-electrode recordings of carp RGC.
The retina was stimulated with a moving pattern of
bright dots on a dark background. Stimulus velocity and
intensity remained constant for one second and then
changed instantaneously in an alternate way. We classi-
fied responses to a subset of the stimuli which could be
naturally ordered. It comprised the combination of three
constant velocities and three light intensity changes,
yielding nine stimulus classes. Analyzing the activity of
114 simultaneously recorded RGC responses, latency
and spike count were used as input variables for both
methods. The performance of each model was measured
as the percentage of correct classification averaged over
eight trials with 16 stimulus repetitions per trial (Fig.1).
Both approaches led to similar results both for the
classification based on the population response combin-
ing all 114 cells and for almost all individual cell
responses. On average, the Bayesian model yielded
slightly higher classification performances than the
regression model for the population estimation and
most of the single cells. Additionally, less CPU time was
needed for the Bayesian analysis. The regression model,
however, yielded on average a slightly lower standard
deviation of correctly classified spike trains. So both
methods proved to be well suited to analyze neural
coding.
* Correspondence: lena.s.koepcke@uni-oldenburg.de
1Institute of Mathematics, University of Oldenburg, D-26111 Oldenburg,
Germany
Full list of author information is available at the end of the article
Figure 1 Comparison of the Bayesian and the regression model.
Average and standard deviation of the percentage of correct
classifications for the cell population and two single cell examples,
one with high and one with average classification performance.
Köpcke et al. BMC Neuroscience 2011, 12(Suppl 1):P178
http://www.biomedcentral.com/1471-2202/12/S1/P178
© 2011 Köpcke et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Comparing both models, the Bayesian approach has
the advantage that it does not require a specific order of
the response and can include prior knowledge of stimu-
lus occurrence. On the other hand, the cumulative pro-
bit model allows for a generalization to higher
dimensional responses without much computational
effort. It also has the advantage to be intuitively compre-
hensible and flexible with respect to the number and
choice of covariates.
Author details
1Institute of Mathematics, University of Oldenburg, D-26111 Oldenburg,
Germany. 2Institute of Biology and Environmental Sciences, University of
Oldenburg, D-26111 Oldenburg, Germany.
Published: 18 July 2011
References
1. Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W: Spikes –
Exploring the Neural Code. The MIT Press; 1999.
2. Zhang K, Ginzburg I, McNaughton BL, Sejnowski TJ: Interpreting neuronal
population activity by reconstruction: Unified framework with
application to hippocampal place cells. Journal of Neurophysiology 1998,
79:1017-1044.
3. Kim K: A Bivariate Cumulative Probit Regression Model for Ordered
Categorical Data. Statistics in Medicine 1995, 14:1341-1352.
doi:10.1186/1471-2202-12-S1-P178
Cite this article as: Köpcke et al.: Comparison of a Bayesian and a
regression model for stimulus classification. BMC Neuroscience 2011 12
(Suppl 1):P178.
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Köpcke et al. BMC Neuroscience 2011, 12(Suppl 1):P178
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A Bivariate Cumulative Probit Regression Model for Ordered Categorical Data. Statistics in Medicine

Sejnowski TJ: Interpreting neuronal population activity by reconstruction: Unified framework with application to hippocampal place cells.

Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color ﬁgure charges • Immediate publication on acceptance

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Comparison of a Bayesian and a regression model for stimulus classification

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