Adaptation in auditory processing.

Adaptation is an essential feature of auditory neurons, which reduces their responses to unchanging and recurring sounds and allows their response properties to be matched to the constantly changing statistics of sounds that reach the ears. As a consequence, processing in the auditory system highlights novel or unpredictable sounds and produces an efficient representation of the vast range of sounds that animals can perceive by continually adjusting the sensitivity and, to a lesser extent, the tuning properties of neurons to the most commonly encountered stimulus values. Together with attentional modulation, adaptation to sound statistics also helps to generate neural representations of sound that are tolerant to background noise and therefore plays a vital role in auditory scene analysis.

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.

Simple transformations capture auditory input to cortex.

Sounds are processed by the ear and central auditory pathway. These processing steps are biologically complex, and many aspects of the transformation from sound waveforms to cortical response remain unclear. To understand this transformation, we combined models of the auditory periphery with various encoding models to predict auditory cortical responses to natural sounds.

Contrast gain control occurs independently of both parvalbumin-positive interneuron activity and shunting inhibition in auditory cortex.

Contrast gain control is the systematic adjustment of neuronal gain in response to the contrast of sensory input. It is widely observed in sensory cortical areas and has been proposed to be a canonical neuronal computation. Here, we investigated whether shunting inhibition from parvalbumin-positive interneurons-a mechanism involved in gain control in visual cortex-also underlies contrast gain control in auditory cortex.

Neural circuits underlying auditory contrast gain control and their perceptual implications.

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. Here we show that neurons in the auditory thalamus and midbrain of mice show robust contrast gain control, and that this is implemented independently of cortical activity.

A dynamic network model of temporal receptive fields 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.

Recent advances in understanding the auditory cortex.

Our ability to make sense of the auditory world results from neural processing that begins in the ear, goes through multiple subcortical areas, and continues in the cortex. The specific contribution of the auditory cortex to this chain of processing is far from understood. Although many of the properties of neurons in the auditory cortex resemble those of subcortical neurons, they show somewhat more complex selectivity for sound features, which is likely to be important for the analysis of natural sounds, such as speech, in real-life listening conditions.

Contrast gain control in mouse auditory cortex.

The neocortex is thought to employ a number of canonical computations, but little is known about whether these computations rely on shared mechanisms across different neural populations. In recent years, the mouse has emerged as a powerful model organism for the dissection of the circuits and mechanisms underlying various aspects of neural processing and therefore provides an important avenue for research into putative canonical computations. One such computation is contrast gain control, the systematic adjustment of neural gain in accordance with the contrast of sensory input, which helps to construct neural representations that are robust to the presence of background stimuli.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded.

Measuring the Performance of Neural Models.

Good metrics of the performance of a statistical or computational model are essential for model comparison and selection. Here, we address the design of performance metrics for models that aim to predict neural responses to sensory inputs. This is particularly difficult because the responses of sensory neurons are inherently variable, even in response to repeated presentations of identical stimuli.

Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing.

Unlabelled: Adaptation to stimulus statistics, such as the mean level and contrast of recently heard sounds, has been demonstrated at various levels of the auditory pathway. It allows the nervous system to operate over the wide range of intensities and contrasts found in the natural world. Yet current standard models of the response properties of auditory neurons do not incorporate such adaptation.

Hearing in noisy environments: noise invariance and contrast gain control.

Contrast gain control has recently been identified as a fundamental property of the auditory system. Electrophysiological recordings in ferrets have shown that neurons continuously adjust their gain (their sensitivity to change in sound level) in response to the contrast of sounds that are heard. At the level of the auditory cortex, these gain changes partly compensate for changes in sound contrast.

Constructing noise-invariant representations of sound in the auditory pathway.

Identifying behaviorally relevant sounds in the presence of background noise is one of the most important and poorly understood challenges faced by the auditory system. An elegant solution to this problem would be for the auditory system to represent sounds in a noise-invariant fashion. Since a major effect of background noise is to alter the statistics of the sounds reaching the ear, noise-invariant representations could be promoted by neurons adapting to stimulus statistics.

Spectrotemporal contrast kernels for neurons in primary auditory cortex.

Auditory neurons are often described in terms of their spectrotemporal receptive fields (STRFs). These map the relationship between features of the sound spectrogram and firing rates of neurons. Recently, we showed that neurons in the primary fields of the ferret auditory cortex are also subject to gain control: when sounds undergo smaller fluctuations in their level over time, the neurons become more sensitive to small-level changes (Rabinowitz et al.

Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1).

Neuronal populations in the primary visual cortex (V1) of mammals exhibit contrast normalization. Neurons that respond strongly to simple visual stimuli - such as sinusoidal gratings - respond less well to the same stimuli when they are presented as part of a more complex stimulus which also excites other, neighboring neurons. This phenomenon is generally attributed to generalized patterns of inhibitory connections between nearby V1 neurons.

Contrast gain control in auditory cortex.

The auditory system must represent sounds with a wide range of statistical properties. One important property is the spectrotemporal contrast in the acoustic environment: the variation in sound pressure in each frequency band, relative to the mean pressure. We show that neurons in ferret auditory cortex rescale their gain to partially compensate for the spectrotemporal contrast of recent stimulation.

Sparse coding in striate and extrastriate visual cortex.

Theoretical studies of mammalian cortex argue that efficient neural codes should be sparse. However, theoretical and experimental studies have used different definitions of the term "sparse" leading to three assumptions about the nature of sparse codes. First, codes that have high lifetime sparseness require few action potentials.

Object vision: a matter of principle.

A new study shows that sparse coding - a principle which elegantly explains neural selectivity in the early visual system - may also explain selectivity in V4, an intermediate visual area implicated in object vision.

Neural representation of natural images in visual area V2.

Area V2 is a major visual processing stage in mammalian visual cortex, but little is currently known about how V2 encodes information during natural vision. To determine how V2 represents natural images, we used a novel nonlinear system identification approach to obtain quantitative estimates of spatial tuning across a large sample of V2 neurons. We compared these tuning estimates with those obtained in area V1, in which the neural code is relatively well understood.

Auditory cortex: representation through sparsification?

The recent discovery of combination-sensitive neurons in the primary auditory cortex of awake marmosets may reconcile previous, apparently contradictory, findings that cortical neurons produce strong, sustained responses, but also represent stimuli sparsely.