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1. Circuit asymmetries underlie functional lateralization in the mouse auditory cortex

   https://doi.org/10.1038/s41467-019-10690-3  

   Levy, Robert B.; Marquarding, Tiemo; Reid, Ashlan P.; Pun, Christopher M.; Renier, Nicolas; Oviedo, Hysell V. (June 2019, Nature Communications)

   Abstract The left hemisphere’s dominance in processing social communication has been known for over a century, but the mechanisms underlying this lateralized cortical function are poorly understood. Here, we compare the structure, function, and development of each auditory cortex (ACx) in the mouse to look for specializations that may underlie lateralization. Using Fos brain volume imaging, we found greater activation in the left ACx in response to vocalizations, while the right ACx responded more to frequency sweeps. In vivo recordings identified hemispheric differences in spectrotemporal selectivity, reinforcing their functional differences. We then compared the synaptic connectivity within each hemisphere and discovered lateralized circuit-motifs that are hearing experience-dependent. Our results suggest a specialist role for the left ACx, focused on facilitating the detection of specific vocalization features, while the right ACx is a generalist with the ability to integrate spectrotemporal features more broadly. 

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2. Sparse high-dimensional decomposition of non-primary auditory cortical receptive fields

   https://doi.org/10.1371/journal.pcbi.1012721  

   Mukherjee, Shoutik; Babadi, Behtash; Shamma, Shihab (January 2025, PLOS Computational Biology)

   Kumar, Arvind (Ed.)

   Characterizing neuronal responses to natural stimuli remains a central goal in sensory neuroscience. In auditory cortical neurons, the stimulus selectivity of elicited spiking activity is summarized by a spectrotemporal receptive field (STRF) that relates neuronal responses to the stimulus spectrogram. Though effective in characterizing primary auditory cortical responses, STRFs of non-primary auditory neurons can be quite intricate, reflecting their mixed selectivity. The complexity of non-primary STRFs hence impedes understanding how acoustic stimulus representations are transformed along the auditory pathway. Here, we focus on the relationship between ferret primary auditory cortex (A1) and a secondary region, dorsal posterior ectosylvian gyrus (PEG). We propose estimating receptive fields in PEG with respect to a well-established high-dimensional computational model of primary-cortical stimulus representations. These “cortical receptive fields” (CortRF) are estimated greedily to identify the salient primary-cortical features modulating spiking responses and in turn related to corresponding spectrotemporal features. Hence, they provide biologically plausible hierarchical decompositions of STRFs in PEG. Such CortRF analysis was applied to PEG neuronal responses to speech and temporally orthogonal ripple combination (TORC) stimuli and, for comparison, to A1 neuronal responses. CortRFs of PEG neurons captured their selectivity to more complex spectrotemporal features than A1 neurons; moreover, CortRF models were more predictive of PEG (but not A1) responses to speech. Our results thus suggest that secondary-cortical stimulus representations can be computed as sparse combinations of primary-cortical features that facilitate encoding natural stimuli. Thus, by adding the primary-cortical representation, we can account for PEG single-unit responses to natural sounds better than bypassing it and considering as input the auditory spectrogram. These results confirm with explicit details the presumed hierarchical organization of the auditory cortex. 

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3. Neural timing of stimulus events with microsecond precision

   https://doi.org/https://doi.org/10.1371/journal.pbio.2006422  

   Luo, J.; Macias, S.; Ness, T.V.; Einevoll, G.T.; Zhang, K.; Moss, C.F. (October 2018, PLoS biology)

   Temporal analysis of sound is fundamental to auditory processing throughout the animal kingdom. Echolocating bats are powerful models for investigating the underlying mechanisms of auditory temporal processing, as they show microsecond precision in discriminating the timing of acoustic events. However, the neural basis for microsecond auditory discrimination in bats has eluded researchers for decades. Combining extracellular recordings in the midbrain inferior colliculus (IC) and mathematical modeling, we show that microsecond precision in registering stimulus events emerges from synchronous neural firing, revealed through low-latency variability of stimulus-evoked extracellular field potentials (EFPs, 200–600 Hz). The temporal precision of the EFP increases with the number of neurons firing in synchrony. Moreover, there is a functional relationship between the temporal precision of the EFP and the spectrotemporal features of the echolocation calls. In addition, EFP can measure the time difference of simulated echolocation call–echo pairs with microsecond precision. We propose that synchronous firing of populations of neurons operates in diverse species to support temporal analysis for auditory localization and complex sound processing. 

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4. Audiovisual Speech Activity Detection with Advanced Long Short-Term Memory

   https://doi.org/10.21437/Interspeech.2018-2490  

   Tao, Fei; Busso, Carlos (September 2018, Interspeech 2018)

   Speech activity detection (SAD) is a key pre-processing step for a speech-based system. The performance of conventional audio-only SAD (A-SAD) systems is impaired by acoustic noise when they are used in practical applications. An alternative approach to address this problem is to include visual information, creating audiovisual speech activity detection (AV-SAD) solutions. In our previous work, we proposed to build an AV-SAD system using bimodal recurrent neural network (BRNN). This framework was able to capture the task-related characteristics in the audio and visual inputs, and model the temporal information within and across modalities. The approach relied on long short-term memory (LSTM). Although LSTM can model longer temporal dependencies with the cells, the effective memory of the units is limited to a few frames, since the recurrent connection only considers the previous frame. For SAD systems, it is important to model longer temporal dependencies to capture the semi-periodic nature of speech conveyed in acoustic and orofacial features. This study proposes to implement a BRNN-based AV-SAD system with advanced LSTMs (A-LSTMs), which overcomes this limitation by including multiple connections to frames in the past. The results show that the proposed framework can significantly outperform the BRNN system trained with the original LSTM layers. 

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5. Aligning Audiovisual Features for Audiovisual Speech Recognition

   https://doi.org/10.1109/ICME.2018.8486455  

   Tao, Fei; Busso, Carlos (July 2018, IEEE International Conference on Multimedia and Expo (ICME))

   Speech activity detection (SAD) is a key pre-processing step for a speech-based system. The performance of conventional audio-only SAD (A-SAD) systems is impaired by acoustic noise when they are used in practical applications. An alternative approach to address this problem is to include visual information, creating audiovisual speech activity detection (AV-SAD) solutions. In our previous work, we proposed to build an AV-SAD system using bimodal recurrent neural network (BRNN). This framework was able to capture the task-related characteristics in the audio and visual inputs, and model the temporal information within and across modalities. The approach relied on long short-term memory (LSTM). Although LSTM can model longer temporal dependencies with the cells, the effective memory of the units is limited to a few frames, since the recurrent connection only considers the previous frame. For SAD systems, it is important to model longer temporal dependencies to capture the semi-periodic nature of speech conveyed in acoustic and orofacial features. This study proposes to implement a BRNN-based AV-SAD system with advanced LSTMs (A-LSTMs), which overcomes this limitation by including multiple connections to frames in the past. The results show that the proposed framework can significantly outperform the BRNN system trained with the original LSTM layers. 

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