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PLoS Comput Biol. 2019 Jan 17;15(1):e1006595. doi: 10.1371/journal.pcbi.1006595. eCollection 2019 Jan.

STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.

Author information

1
Research Center Neurosensory Science, Cluster of Excellence Hearing4all, Department of Medical Physics and Acoustics, University of Oldenburg, Oldenburg, Germany.
2
Zalando Research, Zalando SE, Berlin, Germany.
3
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
4
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
5
Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.
6
Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
7
Microsoft Research, Cambridge, United Kingdom.

Abstract

We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve. Cochleagram components do not superimpose linearly, but rather according to a rule which can be approximated using the max function. This is a consequence of the compression inherent in the cochleagram and the sparsity of natural sounds. Furthermore, cochleagrams do not have negative values. Cochleagrams are therefore not matched well by the assumptions of standard linear approaches such as sparse coding or ICA. We therefore consider a new encoding approach for natural sounds, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data. An efficient truncated EM algorithm is used to fit the MCA model to cochleagram data. We characterize the generative fields (GFs) inferred by MCA with respect to in vivo neural responses in A1 by applying reverse correlation to estimate spectro-temporal receptive fields (STRFs) implied by the learned GFs. Despite the GFs being non-negative, the STRF estimates are found to contain both positive and negative subfields, where the negative subfields can be attributed to explaining away effects as captured by the applied inference method. A direct comparison with ferret A1 shows many similar forms, and the spectral and temporal modulation tuning of both ferret and model STRFs show similar ranges over the population. In summary, our model represents an alternative to linear approaches for biological auditory encoding while it captures salient data properties and links inhibitory subfields to explaining away effects.

PMID:
30653497
PMCID:
PMC6382252
DOI:
10.1371/journal.pcbi.1006595
[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

While the study was conducted, the authors AS and RT were co-affiliated with Zalando SE and Microsoft Research, respectively. These non-academic affiliations had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All data used for the study was collected by the academic affiliations of the authors. All authors have declared that no competing interests exist.

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