Neuronal selectivity to complex vocalization features emerges in the superficial layers of primary auditory cortex.

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how nonselective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from nonselective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in nonselective and feature-selective populations remain open question. In this study, using unanesthetized guinea pigs (GPs), a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in 3 auditory processing stages-the thalamus (ventral medial geniculate body (vMGB)), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call selectivity with about a third of neurons responding to only 1 or 2 call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4, stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1 and set the stage for further mechanistic studies.

Rapid adaptation to elevated extracellular potassium in the pyloric circuit of the crab, Cancer borealis.

Elevated potassium concentration ([K+]) is often used to alter excitability in neurons and networks by shifting the potassium equilibrium potential (EK) and, consequently, the resting membrane potential. We studied the effects of increased extracellular [K+] on the well-described pyloric circuit of the crab Cancer borealis. A 2.5-fold increase in extracellular [K+] (2.5×[K+]) depolarized pyloric dilator (PD) neurons and resulted in short-term loss of their normal bursting activity. This period of silence was followed within 5-10 min by the recovery of spiking and/or bursting activity during continued superfusion of 2.5×[K+] saline. In contrast, when PD neurons were pharmacologically isolated from pyloric presynaptic inputs, they exhibited no transient loss of spiking activity in 2.5×[K+], suggesting the presence of an acute inhibitory effect mediated by circuit interactions. Action potential threshold in PD neurons hyperpolarized during an hour-long exposure to 2.5×[K+] concurrent with the recovery of spiking and/or bursting activity. Thus the initial loss of activity appears to be mediated by synaptic interactions within the network, but the secondary adaptation depends on changes in the intrinsic excitability of the pacemaker neurons. The complex sequence of events in the responses of pyloric neurons to elevated [K+] demonstrates that electrophysiological recordings are necessary to determine both the transient and longer term effects of even modest alterations of K+ concentrations on neuronal activity.NEW & NOTEWORTHY Solutions with elevated extracellular potassium are commonly used as a depolarizing stimulus. We studied the effects of high potassium concentration ([K+]) on the pyloric circuit of the crab stomatogastric ganglion. A 2.5-fold increase in extracellular [K+] caused a transient loss of activity that was not due to depolarization block, followed by a rapid increase in excitability and recovery of spiking within minutes. This suggests that changing extracellular potassium can have complex and nonstationary effects on neuronal circuits.

Quantitative models of auditory cortical processing.

To generate insight from experimental data, it is critical to understand the inter-relationships between individual data points and place them in context within a structured framework. Quantitative modeling can provide the scaffolding for such an endeavor. Our main objective in this review is to provide a primer on the range of quantitative tools available to experimental auditory neuroscientists. Quantitative modeling is advantageous because it can provide a compact summary of observed data, make underlying assumptions explicit, and generate predictions for future experiments. Quantitative models may be developed to characterize or fit observed data, to test theories of how a task may be solved by neural circuits, to determine how observed biophysical details might contribute to measured activity patterns, or to predict how an experimental manipulation would affect neural activity. In complexity, quantitative models can range from those that are highly biophysically realistic and that include detailed simulations at the level of individual synapses, to those that use abstract and simplified neuron models to simulate entire networks. Here, we survey the landscape of recently developed models of auditory cortical processing, highlighting a small selection of models to demonstrate how they help generate insight into the mechanisms of auditory processing. We discuss examples ranging from models that use details of synaptic properties to explain the temporal pattern of cortical responses to those that use modern deep neural networks to gain insight into human fMRI data. We conclude by discussing a biologically realistic and interpretable model that our laboratory has developed to explore aspects of vocalization categorization in the auditory pathway.

Pupillometry to Assess Auditory Sensation in Guinea Pigs.

Noise exposure is a leading cause of sensorineural hearing loss. Animal models of noise-induced hearing loss have generated mechanistic insight into the underlying anatomical and physiological pathologies of hearing loss. However, relating behavioral deficits observed in humans with hearing loss to behavioral deficits in animal models remains challenging. Here, pupillometry is proposed as a method that will enable the direct comparison of animal and human behavioral data. The method is based on a modified oddball paradigm - habituating the subject to the repeated presentation of a stimulus and intermittently presenting a deviant stimulus that varies in some parametric fashion from the repeated stimulus. The fundamental premise is that if the change between the repeated and deviant stimulus is detected by the subject, it will trigger a pupil dilation response that is larger than that elicited by the repeated stimulus. This approach is demonstrated using a vocalization categorization task in guinea pigs, an animal model widely used in auditory research, including in hearing loss studies. By presenting vocalizations from one vocalization category as standard stimuli and a second category as oddball stimuli embedded in noise at various signal-to-noise ratios, it is demonstrated that the magnitude of pupil dilation in response to the oddball category varies monotonically with the signal-to-noise ratio. Growth curve analyses can then be used to characterize the time course and statistical significance of these pupil dilation responses. In this protocol, detailed procedures for acclimating guinea pigs to the setup, conducting pupillometry, and evaluating/analyzing data are described. Although this technique is demonstrated in normal-hearing guinea pigs in this protocol, the method may be used to assess the sensory effects of various forms of hearing loss within each subject. These effects may then be correlated with concurrent electrophysiological measures and post-hoc anatomical observations.

Updates to the guinea pig animal model for in-vivo auditory neuroscience in the low-frequency hearing range.

For gaining insight into general principles of auditory processing, it is critical to choose model organisms whose set of natural behaviors encompasses the processes being investigated. This reasoning has led to the development of a variety of animal models for auditory neuroscience research, such as guinea pigs, gerbils, chinchillas, rabbits, and ferrets; but in recent years, the availability of cutting-edge molecular tools and other methodologies in the mouse model have led to waning interest in these unique model species. As laboratories increasingly look to include in-vivo components in their research programs, a comprehensive description of procedures and techniques for applying some of these modern neuroscience tools to a non-mouse small animal model would enable researchers to leverage unique model species that may be best suited for testing their specific hypotheses. In this manuscript, we describe in detail the methods we have developed to apply these tools to the guinea pig animal model to answer questions regarding the neural processing of complex sounds, such as vocalizations. We describe techniques for vocalization acquisition, behavioral testing, recording of auditory brainstem responses and frequency-following responses, intracranial neural signals including local field potential and single unit activity, and the expression of transgenes allowing for optogenetic manipulation of neural activity, all in awake and head-fixed guinea pigs. We demonstrate the rich datasets at the behavioral and electrophysiological levels that can be obtained using these techniques, underscoring the guinea pig as a versatile animal model for studying complex auditory processing. More generally, the methods described here are applicable to a broad range of small mammals, enabling investigators to address specific auditory processing questions in model organisms that are best suited for answering them.

Vocalization categorization behavior explained by a feature-based auditory categorization model.

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs (GPs) on call categorization tasks using natural calls. We then tested categorization by the model and GPs using temporally and spectrally altered calls. Both the model and GPs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, the model predicted about 50% of the variance in GP behavior. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.

Pupillometry as a reliable metric of auditory detection and discrimination across diverse stimulus paradigms in animal models.

Estimates of detection and discrimination thresholds are often used to explore broad perceptual similarities between human subjects and animal models. Pupillometry shows great promise as a non-invasive, easily-deployable method of comparing human and animal thresholds. Using pupillometry, previous studies in animal models have obtained threshold estimates to simple stimuli such as pure tones, but have not explored whether similar pupil responses can be evoked by complex stimuli, what other stimulus contingencies might affect stimulus-evoked pupil responses, and if pupil responses can be modulated by experience or short-term training. In this study, we used an auditory oddball paradigm to estimate detection and discrimination thresholds across a wide range of stimuli in guinea pigs. We demonstrate that pupillometry yields reliable detection and discrimination thresholds across a range of simple (tones) and complex (conspecific vocalizations) stimuli; that pupil responses can be robustly evoked using different stimulus contingencies (low-level acoustic changes, or higher level categorical changes); and that pupil responses are modulated by short-term training. These results lay the foundation for using pupillometry as a reliable method of estimating thresholds in large experimental cohorts, and unveil the full potential of using pupillometry to explore broad similarities between humans and animal models.