Individual differences in supra-threshold auditory perception - mechanisms and objective correlates

Thesis (Ph.D.)--Boston UniversityTo extract content and meaning from a single source of sound in a quiet background, the auditory system can use a small subset of a very redundant set of spectral and temporal features. In stark contrast, communication in a complex, crowded scene places enormous demands on the auditory system. Spectrotemporal overlap between sounds reduces modulations in the signals at the ears and causes masking, with problems exacerbated by reverberation. Consistent with this idea, many patients seeking audiological treatment seek help precisely because they notice difficulties in environments requiring auditory selective attention. In the laboratory, even listeners with normal hearing thresholds exhibit vast differences in the ability to selectively attend to a target. Understanding the mechanisms causing these supra-threshold differences, the focus of this thesis, may enable research that leads to advances in treating communication disorders that affect an estimated one in five Americans. Converging evidence from human and animal studies points to one potential source of these individual differences: differences in the fidelity with which supra-threshold sound is encoded in the early portions of the auditory pathway. Electrophysiological measures of sound encoding by the auditory brainstem in humans and animals support the idea that the temporal precision of the early auditory neural representation can be poor even when hearing thresholds are normal. Concomitantly, animal studies show that noise exposure and early aging can cause a loss (cochlear neuropathy) of a large percentage of the afferent population of auditory nerve fibers innervating the cochlear hair cells without any significant change in measured audiograms. Using behavioral, otoacoustic and electrophysiological measures in conjunction with computational models of sound processing by the auditory periphery and brainstem, a detailed examination of temporal coding of supra-threshold sound is carried out, focusing on characterizing and understanding individual differences in listeners with normal hearing thresholds and normal cochlear mechanical function. Results support the hypothesis that cochlear neuropathy may reduce encoding precision of supra-threshold sound, and that this manifests as deficits both behaviorally and in subcortical electrophysiological measures in humans. Based on these results, electrophysiological measures are developed that may yield sensitive, fast, objective measures of supra-threshold coding deficits that arise as a result of cochlear neuropathy

Towards a state-space geometry of neural responses to natural scenes: A steady-state approach

Our understanding of information processing by the mammalian visual system has come through a variety of techniques ranging from psychophysics and fMRI to single unit recording and EEG. Each technique provides unique insights into the processing framework of the early visual system. Here, we focus on the nature of the information that is carried by steady state visual evoked potentials (SSVEPs). To study the information provided by SSVEPs, we presented human participants with a population of natural scenes and measured the relative SSVEP response. Rather than focus on particular features of this signal, we focused on the full state-space of possible responses and investigated how the evoked responses are mapped onto this space. Our results show that it is possible to map the relatively high-dimensional signal carried by SSVEPs onto a 2-dimensional space with little loss. We also show that a simple biologically plausible model can account for a high proportion of the explainable variance (~73%) in that space. Finally, we describe a technique for measuring the mutual information that is available about images from SSVEPs. The techniques introduced here represent a new approach to understanding the nature of the information carried by SSVEPs. Crucially, this approach is general and can provide a means of comparing results across different neural recording methods. Altogether, our study sheds light on the encoding principles of early vision and provides a much needed reference point for understanding subsequent transformations of the early visual response space to deeper knowledge structures that link different visual environments

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG

Representation of Sounds in Auditory Cortex of Awake Rats (Dissertation)

This thesis is divided into six chapters (following Introduction). Each chapter was intended to be self-contained, so they do not have to be read in the order they are presented. 

Second chapter (Sec. 2) contains a detailed description of experimental techniques: surgery, recording, and training techniques we used in awake head-fixed rats. We have also included a detailed description of all sets of stimuli we used to probe neurons, analytical methods used to analyze data, and description of computational models used in other parts of the thesis.

Third chapter (Sec. 3) focuses on description of single-neuron responses in primary auditory cortex of awake head-fixed rats. The primary emphasis of this part is on the sparse representation of various auditory stimuli we used to probe neurons, and the heterogeneity of responses of single neurons. To characterize population responses to sound in the auditory cortex we asked the question "What is the typical response to acoustic stimuli?" instead of what is usually asked "What is the stimulus that evokes a response?" We found that the population response was sparse, with many unresponsive neurons. In addition, the responsive neurons showed a great variety of responses. This heterogeneity of neuronal responses ("response zoo," courtesy of Anthony M. Zádor) was, however, surprisingly well characterized by lognormal distribution of firing rates. The observation that firing rates in awake auditory cortex were lognormally distributed was even more interesting given the observation of lognormal distribution of synaptic weights in the cerebral cortex.

The fourth chapter (Sec. 4) focuses on mechanisms which could give rise to lognormal distribution of firing rates, as well as synaptic weights. We proposed specific types of correlations among synaptic connections, and formulated a multiplicative learning rule which led to the observed distributions. We were also able to characterize intracellular activity of neurons in awake auditory cortex. 

The fifth chapter (Sec. 5) contains analysis of so-called up and down states in awake auditory cortex. We show that up and down states--the "signature" subthreshold dynamics so often described in various cortical areas of anesthetized animals--were rare in the primary auditory cortex of awake rats, instead, subthreshold dynamics was consisted of brief, infrequent fluctuations of membrane potential.
The experiments described and analyzed in chapters 2­--4 were conducted in naïve awake rats. As behavior or attention can influence neuronal activity even in primary sensory areas, we developed a setup for head-fixed behavior. 

In the sixth chapter (Sec. 6) we describe the sound discrimination task we have used to study behavior in head-fixed rats. We present a comparison of basic behavioral parameters between restrained and unrestrained rats, as well as evidence of nonauditory modulations of single neuron activity in auditory cortex

A longitudinal study of cortical EEG to olfactory stimulation : involving inter- and intra- subjective responses

This thesis forms the largest and most systematic study of the topographical EEG response to odour. The evolutionary history of the olfactory sense is briefly presented and its relevance to humans in the present day is considered. This thesis examines the information processing that occurs in this sensory system. The type of processing that the olfactory system utilises at each anatomical stage is discussed. The character of olfactory information that may reach neocortical levels in humans is considered in the light of the technology available to detect such information. The neurogenesis of the EEG is considered, together with questions concerning its postulated functional significance. The empirical work carried out uses the most advanced methodology for this type of study. The large number of odourants and subjects, combined with the longitudinal element, make this the most ambitious study of this nature undertaken. The issues surrounding the analysis and interpretation of EEG data arc fully discussed and the impact of Chaos theory is considered. Five major analysis techniques were used on the data collected, but largely negative findings arc reported. The reasons for the failure of this experimental paradigm are discussed and improvements arc suggested for future work. The major contribution of this thesis lies in its exploration of the assumptions of the EEG response to odour. The thesis notes the lack of a conceptual framework that has hindered progress in the area of the "odour" EEG. Recent developments in neural network theory and Chaos theory are highlighted as possible alternative approaches to the modelling and understanding of the olfactory system

Neuromorphic model for sound source segregation

While humans can easily segregate and track a speaker's voice in a loud noisy environment, most modern speech recognition systems still perform poorly in loud background noise. The computational principles behind auditory source segregation in humans is not yet fully understood. In this dissertation, we develop a computational model for source segregation inspired by auditory processing in the brain. To support the key principles behind the computational model, we conduct a series of electro-encephalography experiments using both simple tone-based stimuli and more natural speech stimulus. Most source segregation algorithms utilize some form of prior information about the target speaker or use more than one simultaneous recording of the noisy speech mixtures. Other methods develop models on the noise characteristics. Source segregation of simultaneous speech mixtures with a single microphone recording and no knowledge of the target speaker is still a challenge. Using the principle of temporal coherence, we develop a novel computational model that exploits the difference in the temporal evolution of features that belong to different sources to perform unsupervised monaural source segregation. While using no prior information about the target speaker, this method can gracefully incorporate knowledge about the target speaker to further enhance the segregation.Through a series of EEG experiments we collect neurological evidence to support the principle behind the model. Aside from its unusual structure and computational innovations, the proposed model provides testable hypotheses of the physiological mechanisms of the remarkable perceptual ability of humans to segregate acoustic sources, and of its psychophysical manifestations in navigating complex sensory environments. Results from EEG experiments provide further insights into the assumptions behind the model and provide motivation for future single unit studies that can provide more direct evidence for the principle of temporal coherence