Perinatal exposure to a noncoplanar polychlorinated biphenyl alters tonotopy, receptive fields, and plasticity in rat primary auditory cortex.
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ABSTRACT:
SUBMITTER: Kenet T
PROVIDER: S-EPMC1855918 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:To navigate complex acoustic environments, listeners adapt neural processes to focus on behaviorally relevant sounds in the acoustic foreground while minimizing the impact of distractors in the background, an ability referred to as top-down selective attention. Particularly striking examples of attention-driven plasticity have been reported in primary auditory cortex via dynamic reshaping of spectro-temporal receptive fields (STRFs). By enhancing the neural response to features of the foreground while suppressing those to the background, STRFs can act as adaptive contrast matched filters that directly contribute to an improved cognitive segregation between behaviorally relevant and irrelevant sounds. In this study, we propose a novel discriminative framework for modeling attention-driven plasticity of STRFs in primary auditory cortex. The model describes a general strategy for cortical plasticity via an optimization that maximizes discriminability between the foreground and distractors while maintaining a degree of stability in the cortical representation. The first instantiation of the model describes a form of feature-based attention and yields STRF adaptation patterns consistent with a contrast matched filter previously reported in neurophysiological studies. An extension of the model captures a form of object-based attention, where top-down signals act on an abstracted representation of the sensory input characterized in the modulation domain. The object-based model makes explicit predictions in line with limited neurophysiological data currently available but can be readily evaluated experimentally. Finally, we draw parallels between the model and anatomical circuits reported to be engaged during active attention. The proposed model strongly suggests an interpretation of attention-driven plasticity as a discriminative adaptation operating at the level of sensory cortex, in line with similar strategies previously described across different sensory modalities.
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Project description:Temporal variability of neuronal response characteristics during sensory stimulation is a ubiquitous phenomenon that may reflect processes such as stimulus-driven adaptation, top-down modulation or spontaneous fluctuations. It poses a challenge to functional characterization methods such as the receptive field, since these often assume stationarity. We propose a novel method for estimation of sensory neurons' receptive fields that extends the classic static linear receptive field model to the time-varying case. Here, the long-term estimate of the static receptive field serves as the mean of a probabilistic prior distribution from which the short-term temporally localized receptive field may deviate stochastically with time-varying standard deviation. The derived corresponding generalized linear model permits robust characterization of temporal variability in receptive field structure also for highly non-Gaussian stimulus ensembles. We computed and analyzed short-term auditory spectro-temporal receptive field (STRF) estimates with characteristic temporal resolution 5-30 s based on model simulations and responses from in total 60 single-unit recordings in anesthetized Mongolian gerbil auditory midbrain and cortex. Stimulation was performed with short (100 ms) overlapping frequency-modulated tones. Results demonstrate identification of time-varying STRFs, with obtained predictive model likelihoods exceeding those from baseline static STRF estimation. Quantitative characterization of STRF variability reveals a higher degree thereof in auditory cortex compared to midbrain. Cluster analysis indicates that significant deviations from the long-term static STRF are brief, but reliably estimated. We hypothesize that the observed variability more likely reflects spontaneous or state-dependent internal fluctuations that interact with stimulus-induced processing, rather than experimental or stimulus design.
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Project description:Recent studies have established significant anatomical and functional connections between visual areas and primary auditory cortex (A1), which may be important for cognitive processes such as communication and spatial perception. These studies have raised two important questions: First, which cell populations in A1 respond to visual input and/or are influenced by visual context? Second, which aspects of sound encoding are affected by visual context? To address these questions, we recorded single-unit activity across cortical layers in awake mice during exposure to auditory and visual stimuli. Neurons responsive to visual stimuli were most prevalent in the deep cortical layers and included both excitatory and inhibitory cells. The overwhelming majority of these neurons also responded to sound, indicating unimodal visual neurons are rare in A1. Other neurons for which sound-evoked responses were modulated by visual context were similarly excitatory or inhibitory but more evenly distributed across cortical layers. These modulatory influences almost exclusively affected sustained sound-evoked firing rate (FR) responses or spectrotemporal receptive fields (STRFs); transient FR changes at stimulus onset were rarely modified by visual context. Neuron populations with visually modulated STRFs and sustained FR responses were mostly non-overlapping, suggesting spectrotemporal feature selectivity and overall excitability may be differentially sensitive to visual context. The effects of visual modulation were heterogeneous, increasing and decreasing STRF gain in roughly equal proportions of neurons. Our results indicate visual influences are surprisingly common and diversely expressed throughout layers and cell types in A1, affecting nearly one in five neurons overall.
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Project description:Auditory cortex neurons nonlinearly integrate synaptic inputs from the thalamus and cortex, and generate spiking outputs for simple and complex sounds. Directly comparing synaptic and spiking activity can determine whether this input-output transformation is stimulus-dependent. We employ in vivo whole-cell recordings in the mouse primary auditory cortex, using pure tones and broadband dynamic moving ripple stimuli, to examine properties of functional integration in tonal (TRFs) and spectrotemporal (STRFs) receptive fields. Spectral tuning in STRFs derived from synaptic, subthreshold and spiking responses proves to be substantially more selective than for TRFs. We describe diverse spectral and temporal modulation preferences and distinct nonlinearities, and their modifications between the input and output stages of neural processing. These results characterize specific processing differences at the level of synaptic convergence, integration and spike generation resulting in stimulus-dependent transformation patterns in the primary auditory cortex.
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Project description:As sensory stimuli and behavioral demands change, the attentive brain quickly identifies task-relevant stimuli and associates them with appropriate motor responses. The effects of attention on sensory processing vary across task paradigms, suggesting that the brain may use multiple strategies and mechanisms to highlight attended stimuli and link them to motor action. To better understand factors that contribute to these variable effects, we studied sensory representations in primary auditory cortex (A1) during two instrumental tasks that shared the same auditory discrimination but required different behavioral responses, either approach or avoidance. In the approach task, ferrets were rewarded for licking a spout when they heard a target tone amid a sequence of reference noise sounds. In the avoidance task, they were punished unless they inhibited licking to the target. To explore how these changes in task reward structure influenced attention-driven rapid plasticity in A1, we measured changes in sensory neural responses during behavior. Responses to the target changed selectively during both tasks but did so with opposite sign. Despite the differences in sign, both effects were consistent with a general neural coding strategy that maximizes discriminability between sound classes. The dependence of the direction of plasticity on task suggests that representations in A1 change not only to sharpen representations of task-relevant stimuli but also to amplify responses to stimuli that signal aversive outcomes and lead to behavioral inhibition. Thus, top-down control of sensory processing can be shaped by task reward structure in addition to the required sensory discrimination.
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Project description:Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.
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Project description:Tissue-type plasminogen activator (tPA) is a serine protease that is expressed in various compartments in the brain. It is involved in neuronal plasticity, learning and memory, and addiction. We evaluated whether tPA, exogenously applied, could influence neuroplasticity within the mouse auditory cortex. We used a frequency-pairing paradigm to determine whether neuronal best frequencies shift following the pairing protocol. tPA administration significantly affected the best frequency after pairing, whereby this depended on the pairing frequency relative to the best frequency. When the pairing frequency was above the best frequency, tPA caused a best frequency shift away from the conditioned frequency. tPA significantly widened auditory tuning curves. Our data indicate that regional changes in proteolytic activity within the auditory cortex modulate the fine-tuning of auditory neurons, supporting the function of tPA as a modulator of neuronal plasticity.
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Project description:Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive between cortical neurons and/or bottom-up synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds.
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