Finite deformation model of simple shear of fault with microrotations: apparent strain localisation and en-echelon fracture pattern

Strain localisation is a widespread phenomenon often observed in shear and compressive loading of geomaterials, for example, the fault gouge. It is believed that the main mechanisms of strain localisation are strain softening and mismatch between dilatancy and pressure sensitivity. Observations show that gouge deformation is accompanied by considerable rotations of grains. In our previous work as a model for gouge material, we proposed a continuum description for an assembly of particles of equal radius in which the particle rotation is treated as an independent degree of freedom. We showed that there exist critical values of the model parameters for which the displacement gradient exhibits a pronounced localisation at the mid-surface layers of the fault, even in the absence of inelasticity. Here, we generalise the model to the case of finite deformations characteristic for the gouge deformation. We derive objective constitutive relationships relating the Jaumann rates of stress and moment stress to the relative strain and curvature rates, respectively. The model suggests that the pattern of localisation remains the same as in the linear case. However, the presence of the Jaumann terms leads to the emergence of non-zero normal stresses acting along and perpendicular to the shear layer (with zero hydrostatic pressure), and localised along the mid-line of the gouge; these stress components are absent in the linear model of simple shear. These additional normal stresses, albeit small, cause a change in the direction in which the maximal normal stresses act and in which en-echelon fracturing is formed.

RatSLAM: A hippocampal model for simultaneous localisation and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

Sound source localisation through active audition

This paper presents a novel method for enabling a robot to determine the direction to a sound source through interacting with its environment. The method uses a new neural network, the Parameter-Less Self-Organizing Map algorithm, and reinforcement learning to achieve rapid and accurate response.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

The relative contributions of colour and luminance signals towards the visuomotor localisation of targets in human peripheral vision

We sought to determine the extent to which colour (and luminance) signals contribute towards the visuomotor localization of targets. To do so we exploited the movement-related illusory displacement a small stationary window undergoes when it has a continuously moving carrier grating behind it. We used drifting (1.0-4.2 Hz) red/green-modulated isoluminant gratings or yellow/black luminance-modulated gratings as carriers, each curtailed in space by a stationary, two-dimensional window. After each trial, the perceived location of the window was recorded with reference to an on-screen ruler (perceptual task) or the on-screen touch of a ballistic pointing movement made without visual feedback (visuomotor task). Our results showed that the perceptual displacement measures were similar for each stimulus type and weakly dependent on stimulus drift rate. However, while the visuomotor displacement measures were similar for each stimulus type at low drift rates (<4 Hz), they were significantly larger for luminance than colour stimuli at high drift rates (>4 Hz). We show that the latter cannot be attributed to differences in perceived speed between stimulus types. We assume, therefore, that our visuomotor localization judgements were more susceptible to the (carrier) motion of luminance patterns than colour patterns. We suggest that, far from being detrimental, this susceptibility may indicate the operation of mechanisms designed to counter the temporal asynchrony between perceptual experiences and the physical changes in the environment that give rise to them. We propose that perceptual localisation is equally supported by both colour and luminance signals but that visuomotor localisation is predominantly supported by luminance signals. We discuss the neural pathways that may be involved with visuomotor localization. © 2007 Springer-Verlag.