Locally-Connected, Irregular Deep Neural Networks for Biomimetic Active Vision in a Simulated Human

Masaki Nakada, Honglin Chen, Arjun Lakshmipathy, Demetri Terzopoulos

Abstract: An advanced simulation framework has recently been introduced for exploring human perception and visuomotor control. In this context, we investigate locally-connected, irregular deep neural networks (liNets) for biomimetic active vision. Like commonly used CNNs, liNets are locally-connected, forming receptive fields, but unlike CNNs, they are suitable for spatially irregular photoreceptor distributions inspired by those found in foveated biological retinas. Compared to fully-connected deep neural networks, liNets accommodate a much greater number of retinal photoreceptors to enhance visual acuity without intractable memory consumption. LiNets serve well in the biomimetic active vision system embodied in a simulated human that learns active visuomotor control and active appearance-based recognition.