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2015 ; 11
(12
): e1004628
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The Essential Complexity of Auditory Receptive Fields
#MMPMID26683490
Thorson IL
; Liénard J
; David SV
PLoS Comput Biol
2015[Dec]; 11
(12
): e1004628
PMID26683490
show ga
Encoding properties of sensory neurons are commonly modeled using linear finite
impulse response (FIR) filters. For the auditory system, the FIR filter is
instantiated in the spectro-temporal receptive field (STRF), often in the
framework of the generalized linear model. Despite widespread use of the FIR
STRF, numerous formulations for linear filters are possible that require many
fewer parameters, potentially permitting more efficient and accurate model
estimates. To explore these alternative STRF architectures, we recorded
single-unit neural activity from auditory cortex of awake ferrets during
presentation of natural sound stimuli. We compared performance of > 1000 linear
STRF architectures, evaluating their ability to predict neural responses to a
novel natural stimulus. Many were able to outperform the FIR filter. Two basic
constraints on the architecture lead to the improved performance: (1)
factorization of the STRF matrix into a small number of spectral and temporal
filters and (2) low-dimensional parameterization of the factorized filters. The
best parameterized model was able to outperform the full FIR filter in both
primary and secondary auditory cortex, despite requiring fewer than 30
parameters, about 10% of the number required by the FIR filter. After accounting
for noise from finite data sampling, these STRFs were able to explain an average
of 40% of A1 response variance. The simpler models permitted more straightforward
interpretation of sensory tuning properties. They also showed greater benefit
from incorporating nonlinear terms, such as short term plasticity, that provide
theoretical advances over the linear model. Architectures that minimize parameter
count while maintaining maximum predictive power provide insight into the
essential degrees of freedom governing auditory cortical function. They also
maximize statistical power available for characterizing additional nonlinear
properties that limit current auditory models.