Biology subjects

King, A. J.

Publications and source records attributed to King, A. J..

30 records · Page 2Linked to original sources

bioRxiv2021

Cortical adaptation to sound reverberation

In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.

neuroscience↗

bioRxiv2021

Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis

Autosomal dominant polycystic kidney disease (ADPKD) is the leading genetic cause of end stage renal disease and is characterized by the formation and progressive expansion of kidney cysts. Most ADPKD cases arise from mutations in either the PKD1 or PKD2 gene but the precise downstream signaling pathways driving cyst growth are not well understood, and relatively few studies investigate human cystic kidney due to sample scarcity. In order to better understand the cell types and states driving human ADPKD progression, we analyzed eight ADPKD and five healthy human kidney samples, generating a single cell multiomic atlas consisting of ~100,000 single nucleus transcriptomes and ~50,000 single nucleus epigenomes. The integrated datasets identified 11 primary cell clusters including most epithelial cell types as well as large endothelial and fibroblast cell clusters. Proximal tubular cells from ADPKD kidneys expressed a failed repair transcriptomic signature characterized by profibrotic and proinflammatory transcripts. We identified the G protein-coupled receptor GPRC5A as specifically upregulated in cyst lining cells derived from collecting duct. The principal cell subpopulation enriched for GPRC5A expression also exhibited increased transcription factor binding motif availability for NF-{kappa}B, TEAD, CREB and retinoic acid receptor families and we identified and validated a distal enhancer regulating GPRC5A expression containing these transcription factor binding motifs. This study establishes the single cell transcriptomic and epigenomic landscape of ADPKD, revealing previously unrecognized cellular heterogeneity.

genomics↗

bioRxiv2020

Neural circuits for somatosensory control of auditory thalamocortical processing

Integration of information across the senses is critical for perception and is a common property of neurons in the cerebral cortex, where it is thought to arise primarily from corticocortical connections. Much less is known about the role of subcortical circuits in shaping the multisensory properties of cortical neurons. We show that stimulation of the whiskers causes widespread suppression of sound-evoked activity in mouse primary auditory cortex (A1). This suppression depends on the primary somatosensory cortex (S1), and is implemented through a descending circuit that links S1, via the auditory midbrain, with thalamic neurons that project to A1. Furthermore, a direct pathway from S1 has a facilitatory effect on auditory responses in higher-order thalamic nuclei that project to other brain areas. Crossmodal corticofugal projections to the auditory midbrain and thalamus therefore play a pivotal role in integrating multisensory signals and in enabling communication between different sensory cortical areas.

neuroscience↗

bioRxiv2019

Simple spectral transformations capture the contribution of peripheral processing to cortical responses to natural sounds

Processing in the sensory periphery involves various mechanisms that enable the detection and discrimination of sensory information. Despite their biological complexity, could these processing steps sub-serve a relatively simple transformation of sensory inputs, which are then transmitted to the CNS? Here we explored both biologically-detailed and very simple models of the auditory periphery to find the appropriate input to a phenomenological model of auditory cortical responses to natural sounds. We examined a range of cochlear models, from those involving detailed biophysical characteristics of the cochlea and auditory nerve to very pared-down spectrogram-like approximations of the information processing in these structures. We tested the capacity of these models to predict the time-course of single-unit neural responses recorded in the ferret primary auditory cortex, when combined with a linear non-linear encoding model. We show that a simple model based on a log-spaced, log-scaled power spectrogram with Hill-function compression performs as well as biophysically-detailed models of the cochlea and the auditory nerve. These findings emphasize the value of using appropriate simple models of the periphery when building encoding models of sensory processing in the brain, and imply that the complex properties of the auditory periphery may together result in a simpler than expected functional transformation of the inputs.

neuroscience↗

bioRxiv2019

Complexity of frequency receptive fields predicts tonotopic variability across species

Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these large-scale maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that fractured tonotopy is not unique to rodents. Using two-photon imaging, we found that local variance in frequency preferences is equivalent in ferrets and mice. Much of this heterogeneity was due to neurons with complex frequency tuning, which are less spatially organized than those tuned to a single frequency. Finally, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a bias towards neurons with simple frequency tuning. These results show that local variability in the tonotopic map is not restricted to rodents and help explain inconsistencies in cortical topography across species and recording techniques.

neuroscience↗

bioRxiv2019

Auditory contrast gain control predicts perceptual performance and is not dependent on cortical activity

Neural adaptation enables sensory information to be represented optimally in the brain despite large fluctuations over time in the statistics of the environment. Auditory contrast gain control represents an important example, which is thought to arise primarily from cortical processing. We find, however, that neurons in both the auditory thalamus and midbrain of mice show robust contrast gain control, and that this is implemented independently of cortical activity. Although neurons at each level exhibit contrast gain control to similar degrees, adaptation time constants become longer at later stages of the processing hierarchy, resulting in progressively more stable representations. We also show that auditory discrimination thresholds in human listeners compensate for changes in contrast, and that the strength of this perceptual adaptation can be predicted from physiological measurements. Contrast adaptation is therefore a robust property of both the subcortical and cortical auditory system and accounts for the short-term adaptability of perceptual judgments.

neuroscience↗

bioRxiv2019

Re-weighting of Sound Localization Cues by Audiovisual Training

Sound localization requires the integration in the brain of auditory spatial cues generated by interactions with the external ears, head and body. Perceptual learning studies have shown that the relative weighting of these cues can change in a context-dependent fashion if their relative reliability is altered. One factor that may influence this process is vision, which tends to dominate localization judgments when both modalities are present and induces a recalibration of auditory space if they become misaligned. It is not known, however, whether vision can alter the weighting of individual auditory localization cues. Using non-individualized head-related transfer functions, we measured changes in subjects sound localization biases and binaural localization cue weights after ~55 minutes of training on an audiovisual spatial oddball task. Four different configurations of spatial congruence between visual and auditory cues (interaural time differences (ITDs) and frequency-dependent interaural level differences (interaural level spectra, ILS) were used. When visual cues were spatially congruent with both auditory spatial cues, we observed an improvement in sound localization, as shown by a reduction in the variance of subjects localization biases, which was accompanied by an up-weighting of the more salient ILS cue. However, if the position of either one of the auditory cues was randomized during training, no overall improvement in sound localization occurred. Nevertheless, the spatial gain of whichever cue was matched with vision increased, with different effects observed on the gain for the randomized cue depending on whether ITDs or ILS were matched with vision. As a result, we observed a similar up-weighting in ILS when this cue alone was matched with vision, but no overall change in binaural cue weighting when ITDs corresponded to the visual cues and ILS were randomized. Consistently misaligning both cues with vision produced the ventriloquism aftereffect, i.e., a corresponding shift in auditory localization bias, without affecting the variability of the subjects sound localization judgments, and no overall change in binaural cue weighting. These data show that visual contextual information can invoke a reweighting of auditory localization cues, although concomitant improvements in sound localization are only likely to accompany training with fully congruent audiovisual information.

neuroscience↗

bioRxiv2019

Hierarchical temporal prediction captures motion processing from retina to higher visual cortex

Visual neurons respond selectively to specific features that become increasingly complex in their form and dynamics from the eyes to the cortex. Retinal neurons prefer localized flashing spots of light, primary visual cortical (V1) neurons moving bars, and those in higher cortical areas, such as middle temporal (MT) cortex, favor complex features like moving textures. Whether there are general computational principles behind this diversity of response properties remains unclear. To date, no single normative model has been able to account for the hierarchy of tuning to dynamic inputs along the visual pathway. Here we show that hierarchical application of temporal prediction - representing features that efficiently predict future sensory input from past sensory input - can explain how neuronal tuning properties, particularly those relating to motion, change from retina to higher visual cortex. This suggests that the brain may not have evolved to efficiently represent all incoming information, as implied by some leading theories. Instead, the selective representation of sensory inputs that help in predicting the future may be a general neural coding principle, which when applied hierarchically extracts temporally-structured features that depend on increasingly high-level statistics of the sensory input.

neuroscience↗

bioRxiv2018

Exponentially-decaying temporal integration in a network model can explain much of the neural sensitivity to stimulus history in primary auditory cortex.

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each units response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.\n\nAUTHOR SUMMARYThe responses of neurons in the primary auditory cortex depend on the recent history of sounds over seconds or less. Typically, this dependence on the past has been modelled by applying a wide span of time delays to the input, although this is likely to be biologically unrealistic. Real neurons integrate the history of their activity due to the dynamical properties of their cell membranes and other components. We show that a network with a realistically narrow span of delays and with units having dynamic characteristics like those found in neurons, succinctly models neural responses recorded from ferret primary auditory cortex. Because these integrative properties are widespread, our dynamic network provides a basis for modelling responses in other neural systems.

neuroscience↗

bioRxiv2018

Pitch perception is adapted to species-specific cochlear filtering

Pitch perception is critical for recognizing speech, music and animal vocalizations, but its neurobiological basis remains unsettled, in part because of divergent results from different species. We used a combination of behavioural measurements and cochlear modelling to investigate whether species-specific differences exist in the cues used to perceive pitch and whether these can be accounted for by differences in the auditory periphery. Ferrets performed a pitch discrimination task well whenever temporal envelope cues were robust, but not when resolved harmonics only were available. By contrast, human listeners exhibited the opposite pattern of results on an analogous task, consistent with previous studies. Simulated cochlear responses in the two species suggest that the relative salience of the two types of pitch cues can be attributed to differences in cochlear filter bandwidths. Cross-species variation in pitch perception may therefore reflect the constraints of estimating a sounds fundamental frequency given species-specific cochlear tuning.

neuroscience↗

bioRxiv2017

Sensory cortex is optimised for prediction of future input

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimised to represent features in the recent past of sensory input that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few video or audio frames in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, in their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields tended to resemble those in the brain. This suggests that sensory processing is optimised to extract those features with the most capacity to predict future input.\n\nImpact statementPrediction of future input explains diverse neural tuning properties in sensory cortex.

neuroscience↗

bioRxiv2017

Pitch discrimination performance of ferrets and humans on a go/no-go task.

Animal models are widely used to examine the neurophysiological basis of human pitch perception, and it is therefore important to understand the similarities and differences in pitch processing across species. Pitch discrimination performance is usually measured using two-alternative forced choice (2AFC) procedures in humans and go/no-go tasks in animals, potentially confounding human-to-animal comparisons. We have previously shown that pitch discrimination thresholds of ferrets on a 2AFC task are markedly poorer than those reported for go/no-go tasks in other non-human species (Walker et al., 2009). To better compare the pitch discrimination performance of ferret with other species, here we measure pitch change detection thresholds of ferrets and humans on a common, appetitive go/no-go task design. We found that ferrets pitch thresholds were ~10 times larger than that of humans on the go/no-go task, and were within the range of thresholds reported in other non-human species. Interestingly, ferrets thresholds were 100 times larger than human thresholds on a 2AFC pitch discrimination task using the same stimuli. These results emphasize that sensory discrimination thresholds can differ across tasks, particularly for non-human animals. Performance on our go/no-go task is likely to reflect different neurobiological processes than that on our 2AFC task, as the former required the subjects only to detect a pitch change while the latter required them to label the direction of the pitch change.\n\nABBREVIATIONS\n\nHIGHLIGHTSO_LIPitch discrimination thresholds of ferrets were 10 times larger than those of humans on a go/no-go task\nC_LIO_LIFerrets pitch thresholds are similar to those reported for a range of other mammals\nC_LIO_LIPitch thresholds of ferrets, but not humans, were drastically better on the go/no-go task than a 2AFC task using the same stimuli\nC_LI

animal behavior and cognition↗