Cortical Neural Network Models of Visual Motion Perception for Decision-Making and Reactive Navigation PDF Download

Author: Michael Beyeler
Publisher:
ISBN: 9781339830193
Category :
Languages : en
Pages : 212

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Book Description
Animals use vision to traverse novel cluttered environments with apparent ease. Evidence suggests that the mammalian brain integrates visual motion cues across a number of remote but interconnected brain regions that make up a visual motion pathway. Although much is known about the neural circuitry that is concerned with motion perception in the Primary Visual Cortex (V1) and the Middle Temporal area (MT), little is known about how relevant perceptual variables might be represented in higher-order areas of the motion pathway, and how neural activity in these areas might relate to the behavioral dynamics of locomotion.The main goal of this dissertation is to investigate the computational principles that the mammalian brain might be using to organize low-level motion signals into distributed representations of perceptual variables, and how neural activity in the motion pathway might mediate behavior in reactive navigation tasks. I first investigated how the aperture problem, a fundamental conceptual challenge encountered by all low-level motion systems, can be solved in a spiking neural network model of V1 and MT (consisting of 153,216 neurons and 40 million synapses), relying solely on dynamics and properties gleaned from known electrophysiological and neuroanatomical evidence, and how this neural activity might influence perceptual decision-making. Second, when used with a physical robot performing a reactive navigation task in the real world, I found that the model produced behavioral trajectories that closely matched human psychophysics data. Essential to the success of these studies were software implementations that could execute in real time, which are freely and openly available to the community. Third, using ideas from the efficient-coding and free-energy principles, I demonstrated that a variety of response properties of neurons in the dorsal sub-region of the Medial Superior Temporal area (MSTd) area could be derived from MT-like input features. This finding suggests that response properties such as 3D translation and rotation selectivity, complex motion perception, and heading selectivity might simply be a by-product of MSTd neurons performing dimensionality reduction on their inputs. The hope is that these studies will not only further our understanding of how the brain works, but also lead to novel algorithms and brain-inspired robots capable of outperforming current artificial systems.