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

Willmore, Ben D. B.; Schoppe, Oliver; King, Andrew J.; Schnupp, Jan W. H.; Harper, Nicol S.

doi:10.1523/jneurosci.2441-15.2016

Cited by 48 publications

(95 citation statements)

References 58 publications

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“…We hypothesize two distinct functional methods by which the brain might rapidly estimate the overall mean sound intensity of an environment (that is, its mean intensity over a long duration, such as seconds) to adapt to it, and hence to reach a state of more precise coding. The first of these two methods, the ‘weighted-average method', rapidly estimates the overall mean as the exponentially decaying, weighted-average of the intensities over the very recent past (a ‘sample mean')133. Although this method requires no learning about particular previous environments, the precision of the estimate can be relatively poor.…”

Section: Resultsmentioning

confidence: 99%

Meta-adaptation in the auditory midbrain under cortical influence

2016

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Neural adaptation is central to sensation. Neurons in auditory midbrain, for example, rapidly adapt their firing rates to enhance coding precision of common sound intensities. However, it remains unknown whether this adaptation is fixed, or dynamic and dependent on experience. Here, using guinea pigs as animal models, we report that adaptation accelerates when an environment is re-encountered—in response to a sound environment that repeatedly switches between quiet and loud, midbrain neurons accrue experience to find an efficient code more rapidly. This phenomenon, which we term meta-adaptation, suggests a top–down influence on the midbrain. To test this, we inactivate auditory cortex and find acceleration of adaptation with experience is attenuated, indicating a role for cortex—and its little-understood projections to the midbrain—in modulating meta-adaptation. Given the prevalence of adaptation across organisms and senses, meta-adaptation might be similarly common, with extensive implications for understanding how neurons encode the rapidly changing environments of the real world.

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Section: Resultsmentioning

confidence: 99%

Meta-adaptation in the auditory midbrain under cortical influence

2016

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“…In the anesthetized ferret, responses to speech in noise become increasingly noise invariant from IC to AC, a phenomenon that correlates with estimates of level and contrast adaptation in each region [48]. Further work explicitly modeling these adaptive mechanisms in terms of subtractive synaptic depression and divisive normalization significantly improve AC response predictions and stimulus reconstructions from population responses to noise-corrupted stimuli compared to static LN models [50,52,53]. These results indicate that, in environments with persistent background statistics, neural adaptation reduces responses to the background to optimally encode stimuli with different spectrotemporal profiles.…”

Section: Spectrotemporal Context: Adapting To Noisy Environmentsmentioning

confidence: 99%

Contextual modulation of sound processing in the auditory cortex

Angeloni

2018

Current Opinion in Neurobiology

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In everyday acoustic environments, we navigate through a maze of sounds that possess a complex spectrotemporal structure, spanning many frequencies and exhibiting temporal modulations that differ within frequency bands. Our auditory system needs to efficiently encode the same sounds in a variety of different contexts, while preserving the ability to separate complex sounds within an acoustic scene. Recent work in auditory neuroscience has made substantial progress in studying how sounds are represented in the auditory system under different contexts, demonstrating that auditory processing of seemingly simple acoustic features, such as frequency and time, is highly dependent on co-occurring acoustic and behavioral stimuli. Through a combination of electrophysiological recordings, computational analysis and behavioral techniques, recent research identified the interactions between external spectral and temporal context of stimuli, as well as the internal behavioral state.

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“…However, basic LN models, consisting of just a single STRF and an output nonlinearity, still fail to capture the interactions of sensory filters that are bound to occur naturally in the neural networks of ascending sensory pathways. Recently, more complex and often nonlinear STRF models [20–25] of A1 neurons have achieved improved predictions of experimental data, although sometimes at the expense of being very computationally intensive. These newer models have tended to concentrate on better modeling of features local to the neuron, such as synaptic depression [23] or refractoriness [22].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1–7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.

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

Cited by 48 publications

References 58 publications

Meta-adaptation in the auditory midbrain under cortical influence

Meta-adaptation in the auditory midbrain under cortical influence

Contextual modulation of sound processing in the auditory cortex

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

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