RAPID ADAPTIVE PLASTICITY IN AUDITORY CORTEX

Navigating the acoustic environment entails actively listening for different sound sources, extracting signal from a background of noise, identifying the salient features of a signal and determining what parts of it are relevant. Humans and animals in natural environments perform such acoustic tasks routinely, and have to adapt to changes in the environment and features of the acoustic signals surrounding them in real time. Rapid plasticity has been reported to be a possible mechanism underling the ability to perform these tasks. Previous studies report that neurons in primary auditory cortex (A1) undergo changes in spectro-temporal tuning that enhance the discriminability between different sound classes, modulating their tuning to enhance the task relevant feature. This thesis investigates rapid task related plasticity in two distinct directions; first I investigate the effect of manipulating task difficulty on this type of plasticity. Second I expand the investigation of rapid plasticity into higher order auditory areas. With increasing task difficulty, A1 neurons' response is altered to increasingly suppress the representation of the noise while enhancing the representation of the signal. Comparing adaptive plasticity in secondary auditory cortex (PEG) to A1, PEG neurons further enhance the discriminability of the sound classes by an even greater enhancement of the target response. Taken together these results indicate that adaptive neural plasticity is a plausible mechanism that underlies the performance of novel auditory behaviors in real time, and provide insights into the development of behaviorally significant representation of sound in auditory cortex

Signal detection in animal psychoacoustics: analysis and simulation of sensory and decision-related influences

Signal detection theory (SDT) provides a framework for interpreting psychophysical experiments, separating the putative internal sensory representation and the decision process. SDT was used to analyse ferret behavioural responses in a (yes–no) tone-in-noise detection task. Instead of measuring the receiver-operating characteristic (ROC), we tested SDT by comparing responses collected using two common psychophysical data collection methods. These (Constant Stimuli, Limits) differ in the set of signal levels presented within and across behavioural sessions. The results support the use of SDT as a method of analysis: SDT sensory component was unchanged between the two methods, even though decisions depended on the stimuli presented within a behavioural session. Decision criterion varied trial-by-trial: a ‘yes’ response was more likely after a correct rejection trial than a hit trial. Simulation using an SDT model with several decision components reproduced the experimental observations accurately, leaving only ∼10% of the variance unaccounted for. The model also showed that trial-by-trial dependencies were unlikely to influence measured psychometric functions or thresholds. An additional model component suggested that inattention did not contribute substantially. Further analysis showed that ferrets were changing their decision criteria, almost optimally, to maximise the reward obtained in a session. The data suggest trial-by-trial reward-driven optimization of the decision process. Understanding the factors determining behavioural responses is important for correlating neural activity and behaviour. SDT provides a good account of animal psychoacoustics, and can be validated using standard psychophysical methods and computer simulations, without recourse to ROC measurements

A Corticothalamic Circuit Model for Sound Identification in Complex Scenes

The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal

Interaction between Attention and Bottom-Up Saliency Mediates the Representation of Foreground and Background in an Auditory Scene

Bottom-up (stimulus-driven) and top-down (attentional) processes interact when a complex acoustic scene is parsed. Both modulate the neural representation of the target in a manner strongly correlated with behavioral performance

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far—ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or “priors.” In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available

Dimension-selective attention as a possible driver of dynamic, context-dependent re-weighting in speech processing

The contribution of acoustic dimensions to an auditory percept is dynamically adjusted and reweighted based on prior experience about how informative these dimensions are across the long-term and short-term environment. This is especially evident in speech perception, where listeners differentially weight information across multiple acoustic dimensions, and use this information selectively to update expectations about future sounds. The dynamic and selective adjustment of how acoustic input dimensions contribute to perception has made it tempting to conceive of this as a form of non-spatial auditory selective attention. Here, we review several human speech perception phenomena that might be consistent with auditory selective attention although, as of yet, the literature does not definitively support a mechanistic tie. We relate these human perceptual phenomena to illustrative nonhuman animal neurobiological findings that offer informative guideposts in how to test mechanistic connections. We next present a novel empirical approach that can serve as a methodological bridge from human research to animal neurobiological studies. Finally, we describe four preliminary results that demonstrate its utility in advancing understanding of human non-spatial dimension-based auditory selective attention

Bit Error Rate Analysis of Mobile Ad Hoc Networks over η − µ Fading Channels

In this paper the performance analysis of Mobile ad hoc networks (MANETs) is conducted for a differential QPSK (DQPSK) signals with post-detection equal gain combining (EGC) receiver operating over additive white Gaussian noise (AWGN) as well as for slow frequency nonselective η − µ fading channels, in which the diversity branches can have unequal signal-to-noise ratios (SNRs) as well as different severity parameters. The average bit error probability (ABEP) is evaluated using MGF-based approach. The average BER per multi-hop route of MANETs for this communication is studied