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2015 ; 38
(2
): 315-23
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Neural representation of probabilities for Bayesian inference
#MMPMID25561333
Rich D
; Cazettes F
; Wang Y
; Peña JL
; Fischer BJ
J Comput Neurosci
2015[Apr]; 38
(2
): 315-23
PMID25561333
show ga
Bayesian models are often successful in describing perception and behavior, but
the neural representation of probabilities remains in question. There are several
distinct proposals for the neural representation of probabilities, but they have
not been directly compared in an example system. Here we consider three models: a
non-uniform population code where the stimulus-driven activity and distribution
of preferred stimuli in the population represent a likelihood function and a
prior, respectively; the sampling hypothesis which proposes that the
stimulus-driven activity over time represents a posterior probability and that
the spontaneous activity represents a prior; and the class of models which
propose that a population of neurons represents a posterior probability in a
distributed code. It has been shown that the non-uniform population code model
matches the representation of auditory space generated in the owl's external
nucleus of the inferior colliculus (ICx). However, the alternative models have
not been tested, nor have the three models been directly compared in any system.
Here we tested the three models in the owl's ICx. We found that spontaneous
firing rate and the average stimulus-driven response of these neurons were not
consistent with predictions of the sampling hypothesis. We also found that neural
activity in ICx under varying levels of sensory noise did not reflect a posterior
probability. On the other hand, the responses of ICx neurons were consistent with
the non-uniform population code model. We further show that Bayesian inference
can be implemented in the non-uniform population code model using one spike per
neuron when the population is large and is thus able to support the rapid
inference that is necessary for sound localization.
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