Benjamin Mitchinson

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.

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.

A real-time, FPGA based, biologically plausible neural network processor

A real-time, large scale, leaky-integrate-and-fire neural network processor realized using FPGA is presented. This has been designed, as part of a collaborative project, to investigate and implement biologically plausible models of the rodent vibrissae based somatosensory system to control a robot. An emphasis has been made on hard real-time performance of the processor, as it is to be used as part of a feedback control system. This has led to a revision of some of the established modelling protocols used in other hardware spiking neural network processors. The underlying neuron model has the ability to model synaptic noise and inter-neural propagation delays to provide a greater degree of biological plausibility. The processor has been demonstrated modelling real neural circuitry in real-time, independent of the underlying neural network activity.

Brain-inspired Bayesian perception for biomimetic robot touch

Studies of decision making in animals suggest a neural mechanism of evidence accumulation for competing percepts according to Bayesian sequential analysis. This model of perception is embodied here in a biomimetic tactile sensing robot based on the rodent whisker system. We implement simultaneous perception of object shape and location using two psychological test paradigms: first, a free-response paradigm in which the agent decides when to respond, implemented with Bayesian sequential analysis; and second an interrogative paradigm in which the agent responds after a fixed interval, implemented with maximum likelihood estimation. A benefit of free-response Bayesian perception is that it allows tuning of reaction speed against accuracy. In addition, we find that large gains in decision performance are achieved with unforced responses that allow null decisions on ambiguous data. Therefore free-response Bayesian perception offers benefits for artificial systems that make them more animal-like in behavior.

Spike-timing in primary sensory neurons: a model of somatosensory transduction in the rat

In previous work, we constructed a simple electro-mechanical model of transduction in the rat mystacial follicle that was able to replicate primary afferent response profiles to a variety of whisker deflection stimuli. Here, we update that model to fit newly available spike-timing response data, and demonstrate that the new model produces appropriate responses to richer stimuli, including pseudo white noise and natural textures, at a spike-timing level of detail. Additionally, we demonstrate reliable distributed encoding of multi-component oscillatory signals. No modifications were necessary to the mechanical model of the physical components of the follicle-sinus complex, supporting its generality. We conclude that this model, and its continued development, will aid the understanding both of somatosensory systems in general, and of physiological results from higher (e.g. thalamocortical) systems by accurately characterising the signals on which they operate.

A model of sensorimotor coordination in the rat whisker system

The rat has a sophisticated tactile sensory system centred around the facial whiskers During normal behaviour, rats sweep their longer whiskers (macrovibrissae) through the environment to obtain large-scale information, whilst gathering small-scale information with the sensory apparatus around their snout The macrovibrissae are actively and differentially controlled Using high-speed video recording, we have observed that temporal and spatial parameters of whisking pattern generation are modulated to match environmental features such as the position and orientation of nearby surfaces Whisking is also closely co-ordinated with head and body movements, allowing the animal to locate and orient to interesting stimuli detected through whisker contact In this paper, we present a hybrid (spiking-neuron/arithmetic) model of the neural systems underlying these observed adaptive sensorimotor behaviours, and demonstrate its performance in a simulated robot with rat-like morphology We also report progress towards embedding these control systems in a physical robot with biomimetic whiskers.

Naive Bayes novelty detection for a moving robot with whiskers

Novelty detection would be a useful ability for any autonomous robot that seeks to categorize a new environment or notice unexpected changes in its present one. A biomimetic robot (SCRATCHbot) inspired by the rat whisker system was here used to examine the performance of a novelty detection algorithm based on a “naive” implementation of Bayes rule. Naive Bayes algorithms are known to be both efficient and effective, and also have links with proposed neural mechanisms for decision making. To examine novelty detection, the robot first used its whiskers to sense an empty floor, after which it was tested with a textured strip placed in its path. Given only its experience of the familiar situation, the robot was able to distinguish the novel event and localize it in time. Performance increased with the number of whiskers, indicating benefits from integrating over multiple streams of information. Considering the generality of the algorithm, we suggest that such novelty detection could have widespread applicability as a trigger to react to important features in the robots environment.

Vibrissal Behavior and Function

Tactile hair, or vibrissae, are a mammalian characteristic found on many mammals (Ahl, Veterinary Research Communications 10(4): 245–268. 1986). Vibrissae differ from ordinary (pelagic) hair by being longer and thicker, having large follicles containing blood-filled sinus tissues, and by having an identifiable representation in the somatosensory cortex. Here we provide a brief comparative and ethological review of the role of vibrissae in the life of small terrestrial mammals.