Ben Mitchinson

Feedback control in active sensing: rat exploratory whisking is modulated by environmental contact

Rats sweep their facial whiskers back and forth to generate tactile sensory information through contact with environmental structure. The neural processes operating on the signals arising from these whisker contacts are widely studied as a model of sensing in general, even though detailed knowledge of the natural circumstances under which such signals are generated is lacking. We used digital video tracking and wireless recording of mystacial electromyogram signals to assess the effects of whisker–object contact on whisking in freely moving animals exploring simple environments. Our results show that contact leads to reduced protraction (forward whisker motion) on the side of the animal ipsilateral to an obstruction and increased protraction on the contralateral side. Reduced ipsilateral protraction occurs rapidly and in the same whisk cycle as the initial contact. We conclude that whisker movements are actively controlled so as to increase the likelihood of environmental contacts while constraining such interactions to involve a gentle touch. That whisking pattern generation is under strong feedback control has important implications for understanding the nature of the signals reaching upstream neural processes.

Active vibrissal sensing in rodents and marsupials

In rats, the long facial whiskers (mystacial macrovibrissae) are repetitively and rapidly swept back and forth during exploration in a behaviour known as ‘whisking’. In this paper, we summarize previous evidence from rats, and present new data for rat, mouse and the marsupial grey short-tailed opossum (Monodelphis domestica) showing that whisking in all three species is actively controlled both with respect to movement of the animals body and relative to environmental structure. Using automatic whisker tracking, and Fourier analysis, we first show that the whisking motion of the mystacial vibrissae, in the horizontal plane, can be approximated as a blend of two sinusoids at the fundamental frequency (mean 8.5, 11.3 and 7.3 Hz in rat, mouse and opossum, respectively) and its second harmonic. The oscillation at the second harmonic is particularly strong in mouse (around 22 Hz) consistent with previous reports of fast whisking in that species. In all three species, we found evidence of asymmetric whisking during head turning and following unilateral object contacts consistent with active control of whisker movement. We propose that the presence of active vibrissal touch in both rodents and marsupials suggests that this behavioural capacity emerged at an early stage in the evolution of therian mammals.

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.

Whisker Movements Reveal Spatial Attention: A Unified Computational Model of Active Sensing Control in the Rat

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 animals 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.

Modeling the Emergence of Whisker Direction Maps in Rat Barrel Cortex

Based on measuring responses to rat whiskers as they are mechanically stimulated, one recent study suggests that barrel-related areas in layer 2/3 rat primary somatosensory cortex (S1) contain a pinwheel map of whisker motion directions. Because this map is reminiscent of topographic organization for visual direction in primary visual cortex (V1) of higher mammals, we asked whether the S1 pinwheels could be explained by an input-driven developmental process as is often suggested for V1. We developed a computational model to capture how whisker stimuli are conveyed to supragranular S1, and simulate lateral cortical interactions using an established self-organizing algorithm. Inputs to the model each represent the deflection of a subset of 25 whiskers as they are contacted by a moving stimulus object. The subset of deflected whiskers corresponds with the shape of the stimulus, and the deflection direction corresponds with the movement direction of the stimulus. If these two features of the inputs are correlated during the training of the model, a somatotopically aligned map of direction emerges for each whisker in S1. Predictions of the model that are immediately testable include (1) that somatotopic pinwheel maps of whisker direction exist in adult layer 2/3 barrel cortex for every large whisker on the rats face, even peripheral whiskers; and (2) in the adult, neurons with similar directional tuning are interconnected by a network of horizontal connections, spanning distances of many whisker representations. We also propose specific experiments for testing the predictions of the model by manipulating patterns of whisker inputs experienced during early development. The results suggest that similar intracortical mechanisms guide the development of primate V1 and rat S1.

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.

Design and FPGA implementation of an embedded real-time biologically plausible spiking neural network processor

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.

Digital communications channel equalization using the Kernel Adaline

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 Widrows and Hoffs (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.