Evolving Spiking Neurons from Wheels to Wings

We give an overview of the EPFL indoor flying project, whose goal is to evolve autonomous, adaptive, indoor micro-flyers. Indoor flight is still a challenge because it requires miniaturization, energy efficiency, and smart control. This ongoing project consists in developing an autonomous flying vision-based micro-robot, a bio-inspired controller composed of adaptive spiking neurons directly mapped into digital micro-controllers, and a method to evolve such a network without human intervention. This document describes the motivation and methodology used to reach our goal as well as the results of a number of experiments on vision-based wheeled and flying robots

A biologically inspired spiking model of visual processing for image feature detection

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images

A novel approach to robot vision using a hexagonal grid and spiking neural networks

Many robots use range data to obtain an almost 3-dimensional description of their environment. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Inspired by the structure and behaviour of the human visual system, we present an approach to feature extraction in range data using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields; this approach is a step towards developing a robotic eye that closely mimics the human eye. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonally shaped receptive fields, performance is improved over standard rectangular shaped receptive fields

Vision-based Navigation from Wheels to Wings

We describe an incremental approach towards the development of autonomous indoor flyers that use only vision to navigate in textured environments. In order to cope with the severe weight and energy constraints of such systems, we use spiking neural controllers that can be implemented in tiny micro-controllers and map visual information into motor commands. The network morphology is evolved by means of an evolutionary process on the physical robots. This methodology is tested in three robots of increasing complexity, from a wheeled robot to a dirigible to a winged robot. The paper describes the approach, the robots, their degrees of complexity, and summarizes results. In addition, three compatible electronic boards and a choice of vision sensors suitable for these robots are described in more details. These boards allow a comparative and gradual development of spiking neural controllers for flying robots

Biologically inspired intensity and depth image edge extraction

In recent years artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real-time. Researchers have sought inspiration from biology in order to overcome these challenges resulting in biologically inspired feature extraction methods. By taking inspiration from nature it may be possible to reduce redundancy, extract relevant features, and process an image efficiently by emulating biological visual processes. In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems. Through the use of biologically inspired spiking neural networks we emulate functional computational aspects of biological visual systems. Results demonstrate that the proposed bio-inspired artificial vision system has increased performance over existing computer vision feature extraction approaches

A SURVEY ON COLOR IMAGE SEGMENTATION THROUGH LEAKY INTEGRATE AND FIRE MODEL OF SPIKING NEURAL NETWORKS

Neurological research shows that the biological neurons store information in the timing of spikes. Spiking neural networks are the third generation of neural networks which take into account the precise firing time of neurons for information encoding. In SNNs, computation is performed in the temporal (time related) domain and relies on the timings between spikes. The leaky integrate-and-fire neuron is probably the best-known example of a formal spiking neuron model. In this paper, we have simulated LIF model of SNN for performing the image segmentation using K-Means clustering. Clustering can be termed here as a grouping of similar images in the database. Clustering is done based on different attributes of an image such as size, color, texture etc. The purpose of clustering is to get meaningful result, effective storage and fast retrieval in various areas. Image segmentation is the first step and also one of the most critical tasks of image analysis .Because of its simplicity and efficiency, clustering approach is used for the segmentation of (textured) natural images. After the extraction of the image features using wavelet; the feature samples, handled as vectors, are grouped together in compact but well-separated clusters corresponding to each class of the image. Simulation results therefore demonstrate how SNN can be applied with efficacy in Image Segmentation

Evolutionary Spiking Neural Networks As Racing Car Controllers,"

Abstract-The Izhikevich spiking neural network model is investigated as a method to develop controllers for a simple, but not trivial, car racing game, called TORCS. The controllers are evolved using Evolutionary Programming, and the performance of the best individuals is compared with the hand-coded controller included with the Simulated Car Racing Championship API. A set of experiments using the sigmoid neural network was also conducted, to act as a benchmark for the network of Izhikevich neurons. The results are promising, indicating that this spiking neural network model can be applied to other games or control problems