The Role of GABAergic Inhibition in Processing of lnteraural Time Difference in the Owl's Auditory System

The barn owl uses interaural time differences (ITDs) to localize the azimuthal position of sound. ITDs are processed by an anatomically distinct pathway in the brainstem. Neuronal selectivity for ITD is generated in the nucleus laminaris (NL) and conveyed to both the anterior portion of the ventral nucleus of the lateral lemniscus (VLVa) and the central (ICc) and external (ICx) nuclei of the inferior colliculus. With tonal stimuli, neurons in all regions are found to respond maximally not only to the real ITD, but also to ITDs that differ by integer multiples of the tonal period. This phenomenon, phase ambiguity, does not occur when ICx neurons are stimulated with noise. The main aim of this study was to determine the role of GABAergic inhibition in the processing of ITDs. Selectivity for ITD is similar in the NL and VLVa and improves in the ICc and ICx. Iontophoresis of bicuculline methiodide (BMI), a selective GABAA antagonist, decreased the ITD selectivity of ICc and ICx neurons, but did not affect that of VLVa neurons. Responses of VLVa and ICc neurons to unfavorable ITDs were below the monaural response levels. BMI raised both binaural responses to unfavorable ITDs and monaural responses, though the former remained smaller than the latter. During BMI application, ICx neurons showed phase ambiguity to noise stimuli and no longer responded to a unique ITD. BMI increased the response magnitude and changed the temporal discharge patterns in the VLVa, ICc, and ICx. Iontophoretically applied GABA exerted effects opposite to those of BMI, and the effects could be antagonized with simultaneous application of BMI. These results suggest that GABAergic inhibition (1) sharpens ITD selectivity in the ICc and ICx, (2) contributes to the elimination of phase ambiguity in the ICx, and (3) controls response magnitude and temporal characteristics in the VLVa, ICc, and ICx. Through these actions, GABAergic inhibition shapes the horizontal dimension of the auditory receptive fields

Development of cue integration with reward-mediated learning

This thesis will first introduce in more detail the Bayesian theory and its use in integrating multiple information sources. I will briefly talk about models and their relation to the dynamics of an environment, and how to combine multiple alternative models. Following that I will discuss the experimental findings on multisensory integration in humans and animals. I start with psychophysical results on various forms of tasks and setups, that show that the brain uses and combines information from multiple cues. Specifically, the discussion will focus on the finding that humans integrate this information in a way that is close to the theoretical optimal performance. Special emphasis will be put on results about the developmental aspects of cue integration, highlighting experiments that could show that children do not perform similar to the Bayesian predictions. This section also includes a short summary of experiments on how subjects handle multiple alternative environmental dynamics. I will also talk about neurobiological findings of cells receiving input from multiple receptors both in dedicated brain areas but also primary sensory areas. I will proceed with an overview of existing theories and computational models of multisensory integration. This will be followed by a discussion on reinforcement learning (RL). First I will talk about the original theory including the two different main approaches model-free and model-based reinforcement learning. The important variables will be introduced as well as different algorithmic implementations. Secondly, a short review on the mapping of those theories onto brain and behaviour will be given. I mention the most in uential papers that showed correlations between the activity in certain brain regions with RL variables, most prominently between dopaminergic neurons and temporal difference errors. I will try to motivate, why I think that this theory can help to explain the development of near-optimal cue integration in humans. The next main chapter will introduce our model that learns to solve the task of audio-visual orienting. Many of the results in this section have been published in [Weisswange et al. 2009b,Weisswange et al. 2011]. The model agent starts without any knowledge of the environment and acts based on predictions of rewards, which will be adapted according to the reward signaling the quality of the performed action. I will show that after training this model performs similarly to the prediction of a Bayesian observer. The model can also deal with more complex environments in which it has to deal with multiple possible underlying generating models (perform causal inference). In these experiments I use di#erent formulations of Bayesian observers for comparison with our model, and find that it is most similar to the fully optimal observer doing model averaging. Additional experiments using various alterations to the environment show the ability of the model to react to changes in the input statistics without explicitly representing probability distributions. I will close the chapter with a discussion on the benefits and shortcomings of the model. The thesis continues whith a report on an application of the learning algorithm introduced before to two real world cue integration tasks on a robotic head. For these tasks our system outperforms a commonly used approximation to Bayesian inference, reliability weighted averaging. The approximation is handy because of its computational simplicity, because it relies on certain assumptions that are usually controlled for in a laboratory setting, but these are often not true for real world data. This chapter is based on the paper [Karaoguz et al. 2011]. Our second modeling approach tries to address the neuronal substrates of the learning process for cue integration. I again use a reward based training scheme, but this time implemented as a modulation of synaptic plasticity mechanisms in a recurrent network of binary threshold neurons. I start the chapter with an additional introduction section to discuss recurrent networks and especially the various forms of neuronal plasticity that I will use in the model. The performance on a task similar to that of chapter 3 will be presented together with an analysis of the in uence of different plasticity mechanisms on it. Again benefits and shortcomings and the general potential of the method will be discussed. I will close the thesis with a general conclusion and some ideas about possible future work

Bat azimuthal echolocation using interaural level differences: modeling and implementation by a VLSI-based hardware system

Bats have long fascinated both scientists and engineers due to their superb ability to use echolocation to fly with speed and agility through complex natural environments in complete darkness. This dissertation presents a neuromorphic VLSI circuit model of bat azimuthal echolocation. Interaural level differences (ILDs) are the cues for bat azimuthal echolocation and are also the primary cues used by other mammals to localize high frequency sounds. The fact that neurons in bats respond to short echoes by one or two spikes strongly suggests that the conventionally used firing rate is an unlikely code. The operation of first spike latency in ILD computation and transformation is investigated in a network of spiking neurons linking the lateral superior olive (LSO), dorsal nucleus of the lateral lemniscus (DNLL), and inferior colliculus (IC). The results of the investigation suggest that spatially distributed first spike latencies can serve as a fast code for azimuth that can be ``read-out'' by ascending stages. With the hardware echolocation model that uses spike timing representation, we study how multiple echoes can affect bat echolocation and demonstrate that the response to multiple sounds is not a simple linear addition of the response to single sounds. By developing functional models of the bat echolocation system, we can study the efficient implementation demonstrated by nature. For example, variations among analog VLSI circuit units due to the unavoidable transistor mismatch - traditionally thought of as a hurdle to overcome - have been found beneficial in generating the desired diversity of response that is similar to their neural counterparts. This work advocates the use and design of summating and exponentially decaying synapses. A compact and easily controllable synapse circuit has found an application in achieving a linear temporal spike summation by operating with a very short time constants. It has also been applied in modeling a nonlinear intensity-latency trading by working with a long synaptic time constant. We propose a new synapse circuit model that is compatible with those used in computational models and implementable by CMOS transistors operating in the subthreshold region

Cognitive Analysis of Complex Acoustic Scenes

Natural auditory scenes consist of a rich variety of temporally overlapping sounds that originate from multiple sources and locations and are characterized by distinct acoustic features. It is an important biological task to analyze such complex scenes and extract sounds of interest. The thesis addresses this question, also known as the “cocktail party problem” by developing an approach based on analysis of a novel stochastic signal contrary to deterministic narrowband signals used in previous work. This low-level signal, known as the Stochastic Figure-Ground (SFG) stimulus captures the spectrotemporal complexity of natural sound scenes and enables parametric control of stimulus features. In a series of experiments based on this stimulus, I have investigated specific behavioural and neural correlates of human auditory figure-ground segregation. This thesis is presented in seven sections. Chapter 1 reviews key aspects of auditory processing and existing models of auditory segregation. Chapter 2 presents the principles of the techniques used including psychophysics, modeling, functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Experimental work is presented in the following chapters and covers figure-ground segregation behaviour (Chapter 3), modeling of the SFG stimulus based on a temporal coherence model of auditory perceptual organization (Chapter 4), analysis of brain activity related to detection of salient targets in the SFG stimulus using fMRI (Chapter 5), and MEG respectively (Chapter 6). Finally, Chapter 7 concludes with a general discussion of the results and future directions for research. Overall, this body of work emphasizes the use of stochastic signals for auditory scene analysis and demonstrates an automatic, highly robust segregation mechanism in the auditory system that is sensitive to temporal correlations across frequency channels

Swayed by sound: sonic guidance as a neurorehabilitation strategy in the cerebellar ataxias

Cerebellar disease leads to problems in controlling movement. The most common difficulties are dysmetria and instability when standing. Recent understanding of cerebellar function has expanded to include non -motor aspects such as emotional, cognitive and sensory processing. Deficits in the acquisition and processing of sensory information are one explanation for the movement problems observed in cerebellar ataxia. Sensory deficits result in an inability to make predictions about future events; a primary function of the cerebellum. A question therefore, is whether augmenting or replacing sensory information can improve motor performance in cerebellar disease. This question is tested in this thesis by augmenting sensory information through the provision of an auditory movement guide.A variable described in motor control theory (tau) was used to develop auditory guides that were continuous and dynamic. A reaching experiment using healthy individuals showed that the timing of peak velocity, audiomotor coordination accuracy, and velocity of approach, could be altered in line with the movement parameters embedded in the auditory guides. The thesis then investigated the use of these sonic guides in a clinical population with cerebellar disease. Performance on neurorehabilitation exercises for balance control was tested in twenty people with cerebellar atrophy, with and without auditory guides. Results suggested that continuous, predictive, dynamic auditory guidance is an effective way of improving iii movement smoothness in ataxia (as measured by jerk). In addition, generating and swaying with imaginary auditory guides was also found to increase movement smoothness in cerebellar disease.Following the tests of instantaneous effects, the thesis then investigated the longterm consequences on motor behaviour of following a two -month exercise with auditory guide programme. Seven people with cerebellar atrophy were assessed pre - and post -intervention using two measures, weight -shifting and walking. The results of the weight -shifting test indicated that the sonic -guide exercise programme does not initiate long -term changes in motor behaviour. Whilst there were minor, improvements in walking, because of the weight -shifting results, these could not be attributed to the sonic guides. This finding confirms the difficulties of motor rehabilitation in people with cerebellar disease.This thesis contributes original findings to the field of neurorehabilitation by first showing that on -going and predictive stimuli are an appropriate tool for improving motor behaviour. In addition, the thesis is the first of its kind to apply externally presented guides that convey continuous meaningful information within a clinical population. Finally, findings show that sensory augmentation using the auditory domain is an effective way of improving motor coordination in some forms of cerebellar disease

Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)