Downer, J. D. | Search bioRxiv

bioRxiv2024

Deep Neural Networks Explain Spiking Activity in Auditory Cortex

For static stimuli or at gross ([~]1-s) time scales, artificial neural networks (ANNs) that have been trained on challenging en gineering tasks, like image classification and automatic speech recognition, are now the best predictors of neural responses in primate visual and auditory cortex. It is, however, unknown whether this success can be extended to spiking activity at fine time scales, which are particularly relevant to audition. Here we address this question with ANNs trained on speech audio, and acute multi-electrode recordings from the auditory cortex of squirrel monkeys. We show that layers of trained ANNs can predict the spike counts of neurons responding to speech audio and to monkey vocalizations at bin widths of 50 ms and below. For some neurons, the ANNs explain close to all of the explain able variance--much more than traditional spectrotemporal- receptive-field models, and more than untrained networks. Non-primary neurons tend to be more predictable by deeper layers of the ANNs, but there is much variation by neuron, which would be invisible to coarser recording modalities.

neuroscience↗

bioRxiv2020

An emergent population code in primary auditory cortex supports selective attention to spectral and temporal sound features

Textbook descriptions of primary sensory cortex (PSC) revolve around single neurons representation of low-dimensional sensory features, such as visual object orientation in V1, location of somatic touch in S1, and sound frequency in A1. Typically, studies of PSC measure neurons responses along few (1 or 2) stimulus and/or behavioral dimensions. However, real-world stimuli usually vary along many feature dimensions and behavioral demands change constantly. In order to illuminate how A1 supports flexible perception in rich acoustic environments, we recorded from A1 neurons while rhesus macaques performed a feature-selective attention task. We presented sounds that varied along spectral and temporal feature dimensions (carrier bandwidth and temporal envelope, respectively). Within a block, subjects attended to one feature of the sound in a selective change detection task. We find that single neurons tend to be high-dimensional, in that they exhibit substantial mixed selectivity for both sound features, as well as task context. Contrary to common findings in many previous experiments, attention does not enhance the single-neuron representation of attended features in our data. However, a population-level analysis reveals that ensembles of neurons exhibit enhanced encoding of attended sound features, and this population code tracks subjects performance. Importantly, surrogate neural populations with intact single-neuron tuning but shuffled higher-order correlations among neurons failed to yield attention-related effects observed in the intact data. These results suggest that an emergent population code not measurable at the single-neuron level might constitute the functional unit of sensory representation in PSC. SIGNIFICANCE STATEMENTThe ability to adapt to a dynamic sensory environment promotes a range of important natural behaviors. We recorded from single neurons in monkey primary auditory cortex while subjects attended to either the spectral or temporal features of complex sounds. Surprisingly, we find no average increase in responsiveness to, or encoding of, the attended feature across single neurons. However, when we pool the activity of the sampled neurons via targeted dimensionality reduction, we find enhanced population-level representation of the attended feature and suppression of the distractor feature. This dissociation of the effects of attention at the level of single neurons vs. the population highlights the synergistic nature of cortical sound encoding and enriches our understanding of sensory cortical function.

neuroscience↗