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Many but Not All Deep Neural Network Audio Models Capture Brain Responses and Exhibit Correspondence Between Model Stages and Brain Regions

Greta Tuckute, Jenelle Feather, Dana Boebinger, Josh H. McDermott 

Abstract

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. 

Introduction

An overarching aim of neuroscience is to build quantitatively accurate computational models of sensory systems. Success entails models that take sensory signals as input and reproduce the behavioral judgments mediated by a sensory system as well as its internal representations. A model that can replicate behavior and brain responses for arbitrary stimuli would help validate the theories that underlie the model but would also have a host of important applications. For instance, such models could guide brain-machine interfaces by specifying patterns of brain stimulation needed to elicit particular percepts or behavioral responses. One approach to model building is to construct machine systems that solve biologically relevant tasks, based on the hypothesis that task constraints may cause them to reproduce the characteristics of biological systems [1,2]. Advances in machine learning have stimulated a wave of renewed interest in this model building approach. Specifically, deep artificial neural networks now achieve human-level performance on real-world classification tasks such as object and speech recognition, yielding a new generation of candidate models in vision, audition, language, and other domains [3–8]. 

Methods

Voxel response modeling

The following voxel encoding model methods are adapted from those of Kell and colleagues [31], and where the methods are identical, we have reproduced the analogous sections of the methods verbatim. We summarize the minor differences from the methods of Kell and colleagues [31] at the end of this section. All voxel response modeling and analysis code was written in Python (version 3.6.10), making heavy use of the numpy [134] (version 1.19.0), scipy [135] (version 1.4.1), and scikit-learn [136] libraries (version 0.24.1).

General.

We performed an encoding analysis in which each voxel’s time-averaged activity was predicted by a regularized linear model of the DNN activity. We operationalized each model stage within each candidate model (see section “Candidate models”) as a hypothesis of a neural implementation of auditory processing. The fMRI hemodynamic signal to which we were comparing the candidate model blurs the temporal variation of the cortical response, thus a fair comparison of the model to the fMRI data involved predicting each voxel’s time-averaged response to each sound from time-averaged model responses. We therefore averaged the model responses over the temporal dimension after extraction.

Results

Deep neural network modeling overview

The artificial neural network models considered here take an audio signal as input and transform it via cascades of operations loosely inspired by biology: filtering, pooling, and normalization, among others. Each stage of operations produces a representation of the audio input, typically culminating in an output stage: a set of units whose activations can be interpreted as the probability that the input belongs to a particular class (for instance, a spoken word, or phoneme, or sound category). A model is defined by its “architecture”—the arrangement of operations within the model—and by the parameters of each operation that may be learned during training. These parameters are typically initialized randomly and are then optimized via gradient descent to minimize a loss function over a set of training data. The loss function is typically designed to quantify performance of a task. 

Discussion

We examined similarities between representations learned by contemporary DNN models and those in the human auditory cortex, using regression and representational similarity analyses to compare model and brain responses to natural sounds. We used 2 different brain datasets to evaluate a large set of models trained to perform audio tasks. Most of the models we evaluated produced more accurate brain predictions than a standard spectrotemporal filter model of the auditory cortex [45]. Predictions were consistently much worse for models with permuted weights, indicating a dependence on task-optimized features. The improvement in predictions with model optimization was particularly pronounced for cortical responses in non-primary auditory cortex selective for pitch, speech, or music. Predictions were worse for models trained without background noise. We also observed task-specific prediction improvements for particular brain responses, for instance, with speech tasks producing the best predictions of speech-selective brain responses. Accordingly, the best overall predictions (aggregating across all voxels) were obtained with models trained on multiple tasks. We also found that most models exhibited some degree of correspondence with the presumptive auditory cortical hierarchy, with primary auditory voxels being best predicted by model stages that were consistently earlier than the best-predicting model stages for non-primary voxels. 

Acknowledgments

We thank Ian Griffith for training the music genre classification models, Alex Kell for helpful discussions, Nancy Kanwisher for sharing fMRI data, developers for making their trained models available for public use, and the McDermott lab for comments on an earlier draft of the paper.

Citation: Tuckute G, Feather J, Boebinger D, McDermott JH (2023) Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions. PLoS Biol 21(12): e3002366. https://doi.org/10.1371/journal.pbio.3002366

Academic Editor: David Poeppel, New York University, UNITED STATES

Received: November 3, 2022; Accepted: October 6, 2023; Published: December 13, 2023

Copyright: © 2023 Tuckute et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The code is available from GitHub repo: https://github.com/gretatuckute/auditory_brain_dnn/. An archived version is found at https://zenodo.org/record/8349726 (DOI.org/10.5281/zenodo.8349726). The repository contains a download script allowing the user to download the neural and component data, model activations, result outputs, and fMRI maps.

Funding: This work was supported by the National Institutes of Health (grant R01 DC017970 to JHM, including partial salary support for JHM and JF), an MIT Broshy Fellowship (to GT), an Amazon Science Hub Fellowship (to GT), the American Association of University Women (an International Doctoral Fellowship to GT), the US Department of Energy (Computational Science Graduate Fellowship under grant no. DE-FG02-97ER25308 to JF), and a Friends of the McGovern Institute Fellowship to JF. Each of the fellowships provided partial salary support to the recipient. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AST, Audio Spectrogram Transformer; BLSTM, bidrectional long short-term memory; BOLD, blood-oxygen-level-dependent; DNN, deep neural network; ED, effective dimensionality; ERB, Equivalent Rectangular Bandwidth; fMRI, functional magnetic resonance imaging; HRF, hemodynamic response function; GAN, generative adversarial network; LSTM, long short-term memory; PSC, percent signal change; RDM, representational dissimilarity matrix; RMS, root mean square; ROI, region of interest; RSA, representational similarity analysis; SNR, signal-to-noise ratio; SVM, support vector machine; SWC, Spoken Wikipedia Corpora; S2T, Speech-to-Text; TE, echo time; TR, repetition time; VQ-VAE, vector-quantized variational autoencoder; WSJ, Wall Street Journal

 

https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002366#abstract0

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