Biologically Inspired Navigational Strategies Using Atmospheric Scattering Patterns

A source of accurate and reliable heading is vital for the navigation of autonomous systems such as micro-air vehicles (MAVs). It is desirous that a passive computationally efficient measure of heading is available even when magnetic heading is not. To confront this scenario, a biologically inspired methodology to determine heading based on atmospheric scattering patterns is proposed. A simplified model of the atmosphere is presented, and a hardware analog to the insect Dorsal Rim Area (DRA) photodetection is introduced. Several algorithms are developed to map the patterns of polarized and unpolarized celestial light to heading relative to the sun. Temporal information is used to determine current solar position, and then merged with solar relative heading resulting in absolute heading. Simulation and outdoor experimentation are used to validate the proposed heading determination methodology. Celestial heading measurements are shown to provide closed loop heading control of a ground based robot

SpikePropamine: Differentiable Plasticity in Spiking Neural Networks

The adaptive changes in synaptic efficacy that occur between spiking neurons have been demonstrated to play a critical role in learning for biological neural networks. Despite this source of inspiration, many learning focused applications using Spiking Neural Networks (SNNs) retain static synaptic connections, preventing additional learning after the initial training period. Here, we introduce a framework for simultaneously learning the underlying fixed-weights and the rules governing the dynamics of synaptic plasticity and neuromodulated synaptic plasticity in SNNs through gradient descent. We further demonstrate the capabilities of this framework on a series of challenging benchmarks, learning the parameters of several plasticity rules including BCM, Oja's, and their respective set of neuromodulatory variants. The experimental results display that SNNs augmented with differentiable plasticity are sufficient for solving a set of challenging temporal learning tasks that a traditional SNN fails to solve, even in the presence of significant noise. These networks are also shown to be capable of producing locomotion on a high-dimensional robotic learning task, where near-minimal degradation in performance is observed in the presence of novel conditions not seen during the initial training period