Here we present MIRO, a companion robot designed to engage users in science and robotics via edutainment. MIRO is a robot that is biomimetic in aesthetics, morphology, behaviour, and control architecture. In this paper, we review how these design choices affect its suitability for a companionship role. In particular, we consider how MIRO's emulation of familiar mammalian body language as one component of a broader biomimetic expressive system provides effective communication of emotional state and intent. We go on to discuss how these features contribute to MIRO's potential in other domains such as healthcare, education, and research.

A biomimetic mobile robot called “Shrewbot” has been built as part of a neuroethological study of the mammalian facial whisker sensory system. This platform has been used to further evaluate the problem space of whisker based tactile Simultaneous Localisation And Mapping (tSLAM). Shrewbot uses a biomorphic 3-dimensional array of active whiskers and a model of action selection based on tactile sensory attention to explore a circular walled arena sparsely populated with simple geometric shapes. Datasets taken during this exploration have been used to parameterise an approach to localisation and mapping based on probabilistic occupancy grids. We present the results of this work and conclude that simultaneous localisation and mapping is possible given only noisy odometry and tactile information from a 3-dimensional array of active biomimetic whiskers and no prior information of features in the environment.

Spatial attention is most often investigated in the visual modality through measurement of eye movements, with primates, including humans, a widely-studied model. Its study in laboratory rodents, such as mice and rats, requires different techniques, owing to the lack of a visual fovea and the particular ethological relevance of orienting movements of the snout and the whiskers in these animals. In recent years, several reliable relationships have been observed between environmental and behavioural variables and movements of the whiskers, but the function of these responses, as well as how they integrate, remains unclear. Here, we propose a unifying abstract model of whisker movement control that has as its key variable the region of space that is the animal's current focus of attention, and demonstrate, using computer-simulated behavioral experiments, that the model is consistent with a broad range of experimental observations. A core hypothesis is that the rat explicitly decodes the location in space of whisker contacts and that this representation is used to regulate whisker drive signals. This proposition stands in contrast to earlier proposals that the modulation of whisker movement during exploration is mediated primarily by reflex loops. We go on to argue that the superior colliculus is a candidate neural substrate for the siting of a head-centred map guiding whisker movement, in analogy to current models of visual attention. The proposed model has the potential to offer a more complete understanding of whisker control as well as to highlight the potential of the rodent and its whiskers as a tool for the study of mammalian attention.

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.

The place theory proposed by Jeffress (1948) is still the dominant model of how the brain represents the movement of sensory stimuli between sensory receptors. According to the place theory, delays in signalling between neurons, dependent on the distances between them, compensate for time differences in the stimulation of sensory receptors. Hence the location of neurons, activated by the coincident arrival of multiple signals, reports the stimulus movement velocity. Despite its generality, most evidence for the place theory has been provided by studies of the auditory system of auditory specialists like the barn owl, but in the study of mammalian auditory systems the evidence is inconclusive. We ask to what extent the somatosensory systems of tactile specialists like rats and mice use distance dependent delays between neurons to compute the motion of tactile stimuli between the facial whiskers (or 'vibrissae'). We present a model in which synaptic inputs evoked by whisker deflections arrive at neurons in layer 2/3 (L2/3) somatosensory 'barrel' cortex at different times. The timing of synaptic inputs to each neuron depends on its location relative to sources of input in layer 4 (L4) that represent stimulation of each whisker. Constrained by the geometry and timing of projections from L4 to L2/3, the model can account for a range of experimentally measured responses to two-whisker stimuli. Consistent with that data, responses of model neurons located between the barrels to paired stimulation of two whiskers are greater than the sum of the responses to either whisker input alone. The model predicts that for neurons located closer to either barrel these supralinear responses are tuned for longer inter-whisker stimulation intervals, yielding a topographic map for the inter-whisker deflection interval across the surface of L2/3. This map constitutes a neural place code for the relative timing of sensory stimuli.

In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-fire (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.

Both a FPGA implementation of a rodent's tactile sensory system and a neural FPGA based hardware processor, mimicking the brainstem behaviour are presented. They have principally been designed using biological considerations. The two systems are being ported on a single FPGA platform and will ultimately be embedded on a mobile robot, which will operate in real world environments for object recognition and surface textural discrimination purposes

The implementation of a tactile sensory system for neuromorphic signal processing applications is presented. It has been developed by modelling the structure and behaviour of real rodent facial vibrissae. The primary afferents have been modelled using empirical data taken from electrophysiological measurements, and implemented in real time using hardware processors (DSP/FPGA). Pipelining techniques were employed to maximise the utility of the FPGA hardware. The system is to be integrated into a more complete whisker sensory model, including neural structures within the central nervous system, which can be used to orientate a mobile robot

The implementation of a large scale, leaky-integrate-and-fire neural network processor using the Xilinx Virtex-II family of field programmable gate array (FPGA) is presented. The processor has been designed to model biologically plausible networks of spiking neurons in real-time to assist with the control of a mobile robot. The real-time constraint has led to a re-evaluation of some of the established architectural and algorithmic features of previous spiking neural network based hardware. The design was coded and simulated using Handel-C hardware description language (HDL) and the DK3 design suite from Celoxica. The processor has been physically implemented and tested on a RC200 development board, also from Celoxica.

For transmission of digital data over a linear channel with additive white noise, it can be shown that the optimal symbol-decision equalizer is nonlinear. The Kernel Adaline algorithm, a nonlinear generalization of Widrow's and Hoff's (1960) Adaline, is capable of learning arbitrary nonlinear decision boundaries while retaining the desirable convergence properties of the linear Adaline. This work investigates the use of the Kernel Adaline as an equalizer for such transmission channels. We show that the performance of the Kernel Adaline approaches that of the optimal symbol-decision equalizer given by Bayes theory and further, still produces useful results when the additive noise is nonwhite. A description and preliminary results of an adaptive version of the Kernel Adaline are also presented.