Pennington, J. R. | bioRxiv Science

bioRxiv2022

Can Deep Learning Provide a Generalizable Model for Dynamic Sound Encoding in Auditory Cortex?

Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a general set of computations performed by auditory cortex.

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bioRxiv2020

Dissociation of task engagement and arousal effects in auditory cortex and midbrain

Both generalized arousal and engagement in a specific task influence sensory neural processing. To isolate effects of these state variables in the auditory system, we recorded single-unit activity from primary auditory cortex (A1) and inferior colliculus (IC) of ferrets during a tone detection task, while monitoring arousal via changes in pupil size. We used a generalized linear model to assess the influence of task engagement and pupil size on sound-evoked activity. In both areas, these two variables affected independent neural populations. Pupil size effects were more prominent in IC, while pupil and task engagement effects were equally likely in A1. Task engagement was correlated with larger pupil; thus, some apparent effects of task engagement should in fact be attributed to fluctuations in pupil size. These results indicate a hierarchy of auditory processing, where generalized arousal enhances activity in midbrain, and effects specific to task engagement become more prominent in cortex.

neuroscience↗

bioRxiv2020

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

An important step toward understanding how the brain represents complex natural sounds is to develop accurate models of auditory coding by single neurons. A common model for auditory coding is the linear-nonlinear spectro-temporal receptive field (LN model). The LN model accounts for many features of auditory tuning, but it cannot account for long-lasting effects of sensory context on sound-evoked activity. Two mechanisms that may support these contextual effects are short-term plasticity (STP) and contrast-dependent gain control (GC), each of which has inspired an expanded version of the LN model. Both of these models improve performance over the LN model, but they have never been compared directly. Thus, it is unclear whether they account for distinct processes or describe the same phenomenon in different ways. To address this question, we recorded activity of neurons in primary auditory cortex of awake ferrets during presentation of natural sounds. We then fit models incorporating one nonlinear mechanism (GC or STP) or both (GC+STP) using this single dataset, and measured the correlation between the models predictions and the recorded neural activity. Both the STP and GC models performed significantly better than the LN model, but the GC+STP model performed better than either individual model. We also quantified the similarity between STP and GC model predictions and found only modest equivalence between them. Similar results were observed for a smaller dataset collected in clean and noisy acoustic contexts. These results suggest that the STP and GC models describe distinct, complementary processes in the auditory system. Significance StatementComputational models are used widely to study neural sensory coding. However, models developed in separate studies are often difficult to compare because of differences in stimuli and experimental preparation. This study develops an approach for making systematic comparisons between models that measures the net benefit of incorporating additional nonlinear elements into models of auditory encoding. This approach was then used to compare two different hypotheses for how sensory context, that is, slow changes in the statistics of the acoustic environment, influences activity in auditory cortex. Both models accounted for complementary aspects of the neural response, indicating that a hybrid model incorporating elements of both models provides the most complete characterization of auditory processing.

neuroscience↗