The Essential Complexity of Auditory Receptive Fields

Abstract

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.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons

“…Recent work [67] indicates that choosing different nonlinear output functions from a wide range of plausible candidates has only modest effects on the ability of LN models to capture neural response properties. We therefore did not attempt to systematically explore different types of output nonlinearity or to make the choice of nonlinearity as physiological as possible, but rather focused on an output nonlinearity that is simple, well characterized and widely used in the artificial network literature.…”

Inferring hidden structure in multilayered neural circuits

“…We additionally use a low-rank regularization penalty, that encourages approximately spatiotemporally separable filters [47, 48], a property that is common to receptive fields in a wide variety of sensory systems. In order to fit these models, we use methods based on proximal consensus algorithms (described in Methods).…”

Complementary effects of adaptation and gain control on sound encoding in primary auditory cortex

“…In practice, the filter was applied in two stages: multiplication by an 18x3 spectral weighting matrix followed by convolution with a 3x15 temporal filter. Previous work has shown that this strategy is advantageous (Thorson, Liénard, and David 2015).…”

“…The filterbank included C = 18 filters with f i spaced logarithmically from f low = 200 to f high = 20, 000 Hz. After filtering, the signal was smoothed and down-sampled to 100 Hz to match the temporal bin size of the PSTH, and log compression was applied to account for the action of the cochlea (Thorson, Liénard, and David 2015).…”