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Interactions between auditory statistics processing and visual experience emerge only in late development

ABSTRACT: Summary The auditory system relies on local and global representations to discriminate sounds. This study investigated whether vision influences the development and functioning of these fundamental sound computations. We employed a computational approach to control statistical properties embedded in sounds and tested samples of sighted controls (SC) and congenitally (CB) and late-onset (LB) blind individuals in two experiments. In experiment 1, performance relied on local features analysis; in experiment 2, performance benefited from computing global representations. In both experiments, SC and CB performance remarkably overlapped. Conversely, LB performed systematically worse than the other groups when relying on local features, with no alterations on global representations. Results suggest that auditory computations tested here develop independently from vision. The efficiency of local auditory processing can be hampered in case sight becomes unavailable later in life, supporting the existence of an audiovisual interplay for the processing of auditory details, which emerges only in late development. Graphical abstract Highlights • Computational and deprivation models can be combined to assess sensory plasticity• Basic auditory computations develop independently from early visual input• Late-onset sight loss can hamper the efficiency of local auditory processing Biological sciences; Neuroscience; Sensory neuroscience

SUBMITTER: Berto M

PROVIDER: S-EPMC8593607 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

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Interactions between auditory statistics processing and visual experience emerge only in late development

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