In an ever changing auditory scene, change detection is an ongoing task performed by the auditory brain. Neurons in the midbrain and auditory cortex that exhibit stimulus-specific adaptation (SSA) may contribute to this process. Those neurons adapt to frequent sounds while retaining their excitability to rare sounds. Here, we test whether neurons exhibiting SSA and those without are part of the same networks in the inferior colliculus (IC). We recorded the responses to frequent and rare sounds and then marked the sites of these neurons with a retrograde tracer to correlate the source of projections with the physiological response. SSA neurons were confined to the non-lemniscal subdivisions and exhibited broad receptive fields, while the non-SSA were confined to the central nucleus and displayed narrow receptive fields. SSA neurons receive strong inputs from auditory cortical areas and very poor or even absent projections from the brainstem nuclei. On the contrary, the major sources of inputs to the neurons that lacked SSA were from the brainstem nuclei. These findings demonstrate that auditory cortical inputs are biased in favor of IC synaptic domains that are populated by SSA neurons enabling them to compare top-down signals with incoming sensory information from lower areas.

Pitch is a fundamental attribute in auditory perception involved in source identification and segregation, music, and speech understanding. Pitch percepts are intimately related to harmonic resolvability of sound. When harmonics are well-resolved, the induced pitch is usually salient and precise, and several models relying on autocorrelations or harmonic spectral templates can account for these percepts. However, when harmonics are not completely resolved, the pitch percept becomes less salient, poorly discriminated, with upper range limited to a few hundred hertz, and spectral templates fail to convey percept since only temporal cues are available. Here, a biologically-motivated model is presented that combines spectral and temporal cues to account for both percepts. The model explains how temporal analysis to estimate the pitch of the unresolved harmonics is performed by bandpass filters implemented by resonances in dendritic trees of neurons in the early auditory pathway. It is demonstrated that organizing and exploiting such dendritic tuning can occur spontaneously in response to white noise. This paper then shows how temporal cues of unresolved harmonics may be integrated with spectrally resolved harmonics, creating spectro-temporal harmonic templates for all pitch percepts. Finally, the model extends its account of monaural pitch percepts to pitches evoked by dichotic binaural stimuli.

Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single-to multisite stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25−40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.

word)Natural sounds such as vocalizations often have co-varying acoustic attributes where one acoustic feature can be predicted from another, resulting in redundancy in neural coding. It has been proposed that sensory systems are able to detect such covariation and adapt to reduce redundancy, leading to more efficient neural coding. Results of recent psychoacoustic studies suggest that, following passive exposure to sounds in which temporal and spectral attributes covaried in a correlated fashion, the auditory system adapts to efficiently encode the two co-varying dimensions as a single dimension, at the cost of lost sensitivity to the orthogonal dimension. Here we explore the neural basis of this psychophysical phenomenon by recording single-unit responses from primary auditory cortex (A1) in awake ferrets exposed passively to stimuli with two correlated attributes in the temporal and spectral domain similar to that utilized in the psychoacoustic experiments. We found that: (1) the signal-to-noise (SNR) ratio of spike rate coding of cortical responses driven by sounds with correlated attributes was reduced along the orthogonal dimension; while the SNR ratio remained intact along the exposure dimension; (2) Mutual information of spike temporal coding increased only along the exposure dimension; (3) correlation between neurons tuned to the two covarying attributes decreased after exposure; (4) these exposure effects still occurred if sounds were correlated along two acoustic dimensions, but varied randomly along a third dimension. These neurophysiological results are consistent with the Efficient Learning Hypothesis and may deepen our understanding of how the auditory system represents acoustic regularities and covariance. Significance (119 words)In the Efficient Coding (EC) hypothesis, proposed by Barlow in 1961, the neural code in sensory systems efficiently encodes natural stimuli by minimizing the number of spikes to transmit a sensory signal. Results of recent psychoacoustic studies are consistent with the EC hypothesis, showing that following passive exposure to stimuli with correlated attributes, the auditory system adapts so as to more efficiently encode the two co-varying dimensions as a single dimension. In the current neurophysiological experiments, using a similar stimulus design and experimental paradigm to the psychoacoustic studies of Stilp and colleagues (2010, 2011, 2012, 2016), we recorded responses from single neurons in the auditory cortex of the awake ferret, showing adaptive efficient neural coding of correlated acoustic properties.

Speech recognition in noisy environments can be challenging and requires listeners to accurately segregate a target speaker from irrelevant background noise. Stochastic figure-ground (SFG) tasks in which temporally coherent inharmonic pure-tones must be identified from a background have been used to probe the non-linguistic auditory stream segregation processes important for speech-in-noise processing. However, little is known about the relationship between performance on SFG tasks and speech-in-noise tasks nor the individual differences that may modulate such relationships. In this study, 37 younger normal-hearing adults performed an SFG task with target figure chords consisting of four, six, eight, or ten temporally coherent tones amongst a background of randomly varying tones. Stimuli were designed to be spectrally and temporally flat. An increased number of temporally coherent tones resulted in higher accuracy and faster reaction times (RTs). For ten target tones, faster RTs were associated with better scores on the Quick Speech-in-Noise task. Individual differences in working memory capacity and self-reported musicianship further modulated these relationships. Overall, results demonstrate that the SFG task could serve as an assessment of auditory stream segregation accuracy and RT that is sensitive to individual differences in cognitive and auditory abilities, even among younger normal-hearing adults.