Nonlinearities and Contextual Influences in Auditory Cortical Responses Modeled with Multilinear Spectrotemporal Methods
By Misha B. Ahrens, Jennifer F. Linden, Maneesh Sahani. 2008
148 citations recorded by the source corpus.
The relationship between a sound and its neural representation in the auditory cortex remains elusive. Simple measures such as the frequency response area or frequency tuning curve provide little insight into the function of the auditory cortex in complex sound environments. Spectrotemporal receptive field (STRF) models, despite their descriptive potential, perform poorly when used to predict auditory cortical responses, showing that nonlinear features of cortical response functions, which are not captured by STRFs, are functionally important. We introduce a new approach to the description of auditory cortical responses, using multilinear modeling methods. These descriptions simultaneously account for several nonlinearities in the stimulus–response functions of auditory cortical neurons, including adaptation, spectral interactions, and nonlinear sensitivity to sound level. The models reveal multiple inseparabilities in cortical processing of time lag, frequency, and sound level, and suggest functional mechanisms by which auditory cortical neurons are sensitive to stimulus context. By explicitly modeling these contextual influences, the models are able to predict auditory cortical responses more accurately than are STRF models. In addition, they can explain some forms of stimulus dependence in STRFs that were previously poorly understood.
Subfields: Cognitive Neuroscience, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- David J. Heeger
- Matteo Carandini
- J. E. Elie
- Frédéric E. Theunissen
- Nima Mesgarani
- Stephen V. David
- Xiao-Jing Wang
- Geoffrey E. Hinton
In Signal Processing
- Nima Mesgarani
- Frédéric E. Theunissen
- David J. Heeger
- Matteo Carandini
- J. E. Elie
- Yi Luo
- Jonathan Le Roux
- Yossi Adi
Citations in the corpus
- Normalization as a canonical neural computation (2011, background): “There is also evidence for normalization in cortical areas that are devoted to other sensory modalities, and in particular in primary auditory corte”
- Neural processing of natural sounds (2014, methodology): “Fortunately, advances in regression techniques and machine learning have enabled the estimation of STRFs using natural sounds (FIG.”
- Rapid synaptic depression explains nonlinear modulation of spectro-temporal tuning in primary auditory cortex by natural stimuli (2009, methodology): “Fitting such a model would require a nonlinear regression algorithm outside the scope of the boosting procedure used in this study, such as an iterative procedure that alternately updates STRF coefficients and other nonlinear terms (Ahrens et al., 2008).”
- Contrast Gain Control in Auditory Cortex (2011, unknown): “If we extrapolate from the population data to a hypothetical zero-noise neuron (Sahani and Linden, 2003b; Ahrens et al., 2008), we obtain relative gain values of 2.21 ± 0.16 and 1.40 ± 0.06, respectively (median ± 99% confidence interval) for the same two contrast comparisons.”