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|Title: ||Neural Representation, Learning and Manipulation of Uncertainty|
|Authors: ||Natarajan, Rama|
|Advisor: ||Zemel, Richard S.|
|Department: ||Computer Science|
|Keywords: ||Neural Computation|
|Issue Date: ||21-Apr-2010|
|Abstract: ||Uncertainty is inherent in neural processing due to noise in sensation and the sensory transmission processes, the ill-posed nature of many perceptual tasks, and temporal dynamics of the natural environment, to name a few causes. A wealth of evidence from physiological and behavioral experiments show that these various forms of uncertainty have major effects on perceptual learning and inference. In order to use sensory inputs efficiently to make decisions and guide behavior, neural systems must represent and manipulate information about uncertainty in their computations.
In this thesis, we first consider how spiking neural populations might encode and decode information about continuous dynamic stimulus variables including the uncertainty about them. We explore the efficacy of a complex encoder that is paired with a simple decoder which allows computationally straightforward representation and manipulation of dynamically changing uncertainty. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner, by a simple, local learning rule. We also demonstrate that the coding scheme can be applied recursively to carry out meaningful uncertainty-sensitive computations such as dynamic cue combination.
Next, we explore the computational principles that underlie non-linear response characteristics such as perceptual bias and uncertainty observed in audiovisual spatial illusions that involve multisensory interactions with conflicting cues. We examine in detail, the explanatory power of one particular causal model in characterizing the impact of conflicting inputs on perception and behavior. We also attempt to understand from a computational perspective, whether and how different task instructions might modulate the interaction of information from individual (visual and auditory) senses. Our analyses reveal some new properties of the sensory likelihoods and stimulus prior which were thought to be well described by Gaussian functions. Our results conclude that task-specific expectations can influence perception in ways that relate to a choice of inference strategy.|
|Appears in Collections:||Doctoral|
Department of Computer Science - Doctoral theses
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