Martin J. Pearson

Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach

In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-flre (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the architecture use fixed-point arithmetic and have been implemented using a single field-programmable gate array (FPGA). They have successfully simulated networks of over 1000 neurons configured using biologically plausible models of mammalian neural systems. The neuroprocessor has been designed to be employed primarily for use on mobile robotic vehicles, allowing bio-inspired neural processing models to be integrated directly into real-world control environments. When a neuroprocessor has been designed to act as part of the closed-loop system of a feedback controller, it is imperative to maintain strict real-time performance at all times, in order to maintain integrity of the control system. This resulted in the reevaluation of some of the architectural features of existing hardware for biologically plausible neural networks (NNs). In addition, we describe a development system for rapidly porting an underlying model (based on floating-point arithmetic) to the fixed-point representation of the FPGA-based neuroprocessor, thereby allowing validation of the hardware architecture. The developmental system environment facilitates the cooperation of computational neuroscientists and engineers working on embodied (robotic) systems with neural controllers, as demonstrated by our own experience on the Whiskerbot project, in which we developed models of the rodent whisker sensory system.

Whisking with robots

This article summarizes some of the key features of the rat vibrissal system, including the actively controlled sweeping movements of the vibrissae known as whisking, and reviews the past and ongoing research aimed at replicating some of this functionality in biomimetic robots.

Whiskerbot: A Robotic Active Touch System Modeled on the Rat Whisker Sensory System

The Whiskerbot project is a collaborative project between robotics engineers, computational neuroscientists and ethologists, aiming to build a biologically inspired robotic implementation of the rodent whisker sensory system. The morphology and mechanics of the large whiskers (macro-vibrissae) have been modeled, as have the neural structures that constitute the rodent central nervous system responsible for macro-vibrissae sensory processing. There are two principal motivations for this project. First, by implementing an artificial whisker sensory system controlled using biologically plausible neural networks we hope to test existing models more thoroughly and develop new hypotheses for vibrissal sensory processing. Second, the sensory mode of tactile whiskers could be useful for general mobile robotic sensory deployment. In this article the robotic platform that has been built is detailed as well as some of the experiments that have been conducted to test the neural control algorithms and architectures inspired from neuroethological observations to mediate adaptive behaviors.

Empirically inspired simulated electro-mechanical model of the rat mystacial follicle-sinus complex

In whiskered animals, activity is evoked in the primary sensory afferent cells (trigeminal nerve) by mechanical stimulation of the whiskers. In some cell populations this activity is correlated well with continuous stimulus parameters such as whisker deflection magnitude, but in others it is observed to represent events such as whisker–stimulator contact or detachment. The transduction process is mediated by the mechanics of the whisker shaft and follicle–sinus complex (FSC), and the mechanics and electro–chemistry of mechanoreceptors within the FSC. An understanding of this transduction process and the nature of the primary neural codes generated is crucial for understanding more central sensory processing in the thalamus and cortex. However, the details of the peripheral processing are currently poorly understood. To overcome this deficiency in our knowledge, we constructed a simulated electro–mechanical model of the whisker–FSC–mechanoreceptor system in the rat and tested it against a variety of data drawn from the literature. The agreement was good enough to suggest that the model captures many of the key features of the peripheral whisker system in the rat.

Biomimetic vibrissal sensing for robots.

Active vibrissal touch can be used to replace or to supplement sensory systems such as computer vision and, therefore, improve the sensory capacity of mobile robots. This paper describes how arrays of whisker-like touch sensors have been incorporated onto mobile robot platforms taking inspiration from biology for their morphology and control. There were two motivations for this work: first, to build a physical platform on which to model, and therefore test, recent neuroethological hypotheses about vibrissal touch; second, to exploit the control strategies and morphology observed in the biological analogue to maximize the quality and quantity of tactile sensory information derived from the artificial whisker array. We describe the design of a new whiskered robot, Shrewbot, endowed with a biomimetic array of individually controlled whiskers and a neuroethologically inspired whisking pattern generation mechanism. We then present results showing how the morphology of the whisker array shapes the sensory surface surrounding the robots head, and demonstrate the impact of active touch control on the sensory information that can be acquired by the robot. We show that adopting bio-inspired, low latency motor control of the rhythmic motion of the whiskers in response to contact-induced stimuli usefully constrains the sensory range, while also maximizing the number of whisker contacts. The robot experiments also demonstrate that the sensory consequences of active touch control can be usefully investigated in biomimetic robots.

Tactile Discrimination Using Active Whisker Sensors

We describe a novel, biomimetic tactile sensing system modeled on the facial whiskers (vibrissae) of animals such as rats and mice. The “BIOTACT Sensor” consists of a conical array of modular, actuated hair-like elements, each instrumented at the base to accurately detect deflections of the shaft by whisker-surface contacts. A notable characteristic of this array is that, like the biological sensory system it mimics, the whiskers are moved back-and-forth (“whisked”) so as to make repeated, brief contacts with surfaces of interest. Furthermore, these movements are feedback-modulated in a manner intended to emulate some of the “active sensing” control strategies observed in whiskered animals. We show that accurate classification of surface texture using data obtained from whisking against three different surfaces is achievable using classifiers based on either naive Bayes or template methods. Notably, the performance of both these approaches to classify textures after training on as few as one or two surface contacts was improved when the whisking motion was controlled using a sensory feedback mechanism. We conclude that active vibrissal sensing could likewise be a useful sensory capacity for autonomous robots.

Contact type dependency of texture classification in a whiskered mobile robot

Actuated artificial whiskers modeled on rat macrovibrissae can provide effective tactile sensor systems for autonomous robots. This article focuses on texture classification using artificial whiskers and addresses a limitation of previous studies, namely, their use of whisker deflection signals obtained under relatively constrained experimental conditions. Here we consider the classification of signals obtained from a whiskered robot required to explore different surface textures from a range of orientations and distances. This procedure resulted in a variety of deflection signals for any given texture. Using a standard Gaussian classifier we show, using both hand-picked features and ones derived from studies of rat vibrissal processing, that a robust rough-smooth discrimination is achievable without any knowledge of how the whisker interacts with the investigated object. On the other hand, finer discriminations appear to require knowledge of the target’s relative position and/or of the manner in which the whisker contact its surface.

Tactile SLAM with a biomimetic whiskered robot

Future robots may need to navigate where visual sensors fail. Touch sensors provide an alternative modality, largely unexplored in the context of robotic map building. We present the first results in grid based simultaneous localisation and mapping (SLAM) with biomimetic whisker sensors, and show how multi-whisker features coupled with priors about straight edges in the world can boost its performance. Our results are from a simple, small environment but are intended as a first baseline to measure future algorithms against.

Adaptive Cancelation of Self-Generated Sensory Signals in a Whisking Robot

Sensory signals are often caused by ones own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme.

SCRATCHbot: active tactile sensing in a whiskered mobile robot

The rodent vibrissal (whisker) system is one of the most widely investigated model sensory systems in neuroscience owing to its discrete organisation from the sensory apparatus (the whisker shaft) all the way to the sensory cortex, its ease of manipulation, and its presence in common laboratory animals. Neurobiology shows us that the brain nuclei and circuits that process vibrissal touch signals, and that control the positioning and movement of the whiskers, form a neural architecture that is a good model of how the mammalian brain, in general, coordinates sensing with action. In this paper we describe SCRATCHbot, a biomimetic robot based on the rat whisker system, and show how this robot is providing insight into the operation of neural systems underlying vibrissal control, and is helping us to understand the active sensing strategies that animals employ in order to boost the quality and quantity of information provided by their sensory organs.