Authored Books

D. Mareschal, M. H. Johnson, S. Siros, M. W. Spratling, M. S. C. Thomas and G. Westermann (2007) Neuroconstructivism: How the Brain Constructs Cognition, Oxford University Press: Oxford, UK.

Journal Articles

M. W. Spratling (2010) Predictive coding as a model of response properties in cortical area V1. Journal of Neuroscience, 30(9):3531-43. CODE
Abstract
A simple model is shown to account for a large range of V1 classical, and non-classical, receptive field properties including orientation-tuning, spatial and temporal frequency tuning, cross-orientation suppression, surround suppression, and facilitation and inhibition by flankers and textured surrounds. The model is an implementation of the predictive coding theory of cortical function and thus provides a single computational explanation for a diverse range of neurophysiological findings. Furthermore, since predictive coding can be related to the biased competition theory and is a specific example of more general theories of hierarchical perceptual inference the current results relate V1 response properties to a wider, more unified, framework for understanding cortical function.


M. W. Spratling (2009) Learning posture invariant spatial representations through temporal correlations. IEEE Transactions on Autonomous Mental Development, 1(4):253-63. PDF
Abstract
A hierarchical neural network model is used to learn, without supervision, sensory-sensory coordinate transformations like those believed to be encoded in the dorsal pathway of the cerebral cortex. The resulting representations of visual space are invariant to eye orientation, neck orientation, or posture in general. These posture invariant spatial representations are learned using the same mechanisms that have previously been proposed to operate in the cortical ventral pathway to learn object representation that are invariant to translation, scale, orientation, or viewpoint in general. This model thus suggests that the same mechanisms of learning and development operate across multiple cortical hierarchies.


K. De Meyer and M. W. Spratling (2009) A model of non-linear interactions between cortical top-down and horizontal connections explains the attentional gating of collinear facilitation. Vision Research, 49(5):553-68. PDF
Abstract
Past physiological and psychophysical experiments have shown that attention can modulate the effects of contextual information appearing outside the classical receptive field of a cortical neuron. Specifically, it has been suggested that attention, operating via cortical feedback connections, gates the effects of long-range horizontal connections underlying collinear facilitation in cortical area V1. This article proposes a novel mechanism, based on the computations performed within the dendrites of cortical pyramidal cells, that can account for these observations. Furthermore, it is shown that the top-down gating signal into V1 can result from a process of biased competition occurring in extrastriate cortex. A model based on these two assumptions is used to replicate the results of physiological and psychophysical experiments on collinear facilitation and attentional modulation.


M. W. Spratling, K. De Meyer and R. Kompass (2009) Unsupervised learning of overlapping image components using divisive input modulation. Computational Intelligence and Neuroscience, 2009(381457):1-19. PDFCODE
Abstract
This paper demonstrates that non-negative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance.


M. W. Spratling (2008) Predictive coding as a model of biased competition in visual attention. Vision Research, 48(12):1391-408. PDFCODE
Abstract
Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of cortical function which proposes that feedback acts to suppress information predicted by higher-level cortical regions. Despite this discrepancy, this article demonstrates that the predictive coding model can be used to simulate a number of the effects of attention. This is achieved via a simple mathematical rearrangement of the predictive coding model, which allows it to be interpreted as a form of biased competition model. Nonlinear extensions to the model are proposed that enable it to explain a wider range of data.


M. W. Spratling (2008) Reconciling predictive coding and biased competition models of cortical function. Frontiers in Computational Neuroscience, 2(4):1-8. PDF
Abstract
A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model.


M. S. C. Thomas, G. Westermann, D. Mareschal M. H. Johnson, S. Siros and M. W. Spratling (2008) Studying development in the 21st century [response to commentaries]. Behavioral and Brain Sciences, 31(3):345-56.
Abstract
In this response, we consider four main issues arising from the commentaries to the target article. These include further details of the theory of interactive specialization, the relationship between neuroconstructivism and selectionism, the implications of neuroconstructivism for the notion of representation, and the role of genetics in theories of development. We conclude by stressing the importance of multidisciplinary approaches in the future study of cognitive development and by identifying the directions in which neuroconstructivism can expand in the Twenty-first Century.


S. Siros, M. W. Spratling, M. S. C. Thomas, G. Westermann, D. Mareschal and M. H. Johnson (2008) Précis of Neuroconstructivism: how the brain constructs cognition. Behavioral and Brain Sciences, 31(3):321-31. PDF
Abstract
Neuroconstructivism proposes a unifying framework for the study of development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behaviour), and (3) computational modelling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy, which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, the development of dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in neural structures leading to partial representations across multiple brain regions and timescales.


G. Westermann, D. Mareschal, M. H. Johnson, S. Siros, M. W. Spratling and M. S. C. Thomas (2007) Neuroconstructivism. Developmental Science, 10(1):75-83. PDF
Abstract
Neuroconstructivism is a theoretical framework focusing on the construction of representation in the developing brain. Cognitive development is explained as emerging from the experience-dependent development of neural structures supporting mental representations. Neural development occurs in the context of multiple interacting constraints acting on different levels, from the individual cell to the external environment of the developing child. Cognitive development can thus be understood as a trajectory originating from the constraints on the underlying neural structures. This perspective offers an integrated view of normal and abnormal development as well as of development and adult processing, and it stands apart from traditional cognitive approaches in taking seriously the constraints on cognition inherent by the substrate that delivers it.


M. W. Spratling (2006) Learning image components for object recognition. Journal of Machine Learning Research, 7:793-815. PDF
Abstract
In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or features. Non-negative matrix factorisation is a generative model that has been specifically proposed for finding such meaningful representations of image data, through the use of non-negativity constraints on the factors. This article reports on an empirical investigation of the performance of non-negative matrix factorisation algorithms. It is found that such algorithms need to impose additional constraints on the sparseness of the factors in order to successfully deal with occlusion. However, these constraints can themselves result in these algorithms failing to identify image components under certain conditions. In contrast, a recognition model (a competitive learning neural network algorithm) reliably and accurately learns representations of elementary image features without such constraints.


M. W. Spratling and M. H. Johnson (2006) A feedback model of perceptual learning and categorisation. Visual Cognition, 13(2):129-65. PDF
Abstract
Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This article employs a computational model of the cortical region interactions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorised and could also facilitate the learning of sub-ordinate level representations suitable for object identification and perceptual expertise.


M. W. Spratling (2005) Learning viewpoint invariant perceptual representations from cluttered images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):753-61. PDF
Abstract
In order to perform object recognition, it is necessary to form perceptual representations that are sufficiently specific to distinguish between objects, but that are also sufficiently flexible to generalise across changes in location, rotation and scale. A standard method for learning perceptual representations that are invariant to viewpoint is to form temporal associations across image sequences showing object transformations. However, this method requires that individual stimuli are presented in isolation and is therefore unlikely to succeed in real-world applications where multiple objects can co-occur in the visual input. This article proposes a simple modification to the learning method, that can overcome this limitation, and results in more robust learning of invariant representations.


M. W. Spratling (2004) Local versus distributed: a poor taxonomy of neural coding strategies [commentary]. Behavioral and Brain Sciences, 27(5):700-2. PDF

M. W. Spratling and M. H. Johnson (2004) Neural coding strategies and mechanisms of competition. Cognitive Systems Research, 5(2):93-117. PDF
Abstract
A long running debate has concerned the question of whether neural representations are encoded using a distributed or a local coding scheme. In both schemes individual neurons respond to certain specific patterns of pre-synaptic activity. Hence, rather than being dichotomous, both coding schemes are based on the same representational mechanism. We argue that a population of neurons needs to be capable of learning both local and distributed representations, as appropriate to the task, and should be capable of generating both local and distributed codes in response to different stimuli. Many neural network algorithms, which are often employed as models of cognitive processes, fail to meet all these requirements. In contrast, we present a neural network architecture which enables a single algorithm to efficiently learn, and respond using, both types of coding scheme.


M. W. Spratling and M. H. Johnson (2004) A feedback model of visual attention. Journal of Cognitive Neuroscience, 16(2):219-37. PDF
Abstract
Feedback connections are a prominent feature of cortical anatomy and are likely to have a significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research.


M. W. Spratling and M. H. Johnson (2003) Exploring the functional significance of dendritic inhibition in cortical pyramidal cells. Neurocomputing, 52-54:389-95. PDF
Abstract
Inhibitory synapses contacting the soma and axon initial segment are commonly presumed to participate in shaping the response properties of cortical pyramidal cells. Such an inhibitory mechanism has been explored in numerous computational models. However, the majority of inhibitory synapses target the dendrites of pyramidal cells, and recent physiological data suggests that this dendritic inhibition affects tuning properties. We describe a model that can be used to investigate the role of dendritic inhibition in the competition between neurons. With this model we demonstrate that dendritic inhibition significantly enhances the computational and representational properties of neural networks.


M. W. Spratling (2002) Cortical region interactions and the functional role of apical dendrites. Behavioral and Cognitive Neuroscience Reviews, 1(3):219-28. PDF
Abstract
The basal and distal apical dendrites of pyramidal cells occupy distinct cortical layers and are targeted by axons originating in different cortical regions. Hence, apical and basal dendrites receive information from distinct sources. Physiological evidence suggests that this anatomically observed segregation of input sources may have functional significance. This possibility has been explored in various connectionist models that employ neurons with functionally distinct apical and basal compartments. A neuron in which separate sets of inputs can be integrated independently has the potential to operate in a variety of ways which are not possible for the conventional model of a neuron in which all inputs are treated equally. This article thus considers how functionally distinct apical and basal dendrites can contribute to the information processing capacities of single neurons and, in particular, how information from different cortical regions could have disparate affects on neural activity and learning.


M. W. Spratling and M. H. Johnson (2002) Pre-integration lateral inhibition enhances unsupervised learning. Neural Computation, 14(9):2157-79. PDF
Abstract
A large and influential class of neural network architectures use post-integration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through pre-integration lateral inhibition, does provide appropriate coding properties and can be used to efficiently learn such representations. Furthermore, this architecture is consistent with both neuro-anatomical and neuro-physiological data. We thus argue that pre-integration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible.


M. W. Spratling and M. H. Johnson (2001) Dendritic inhibition enhances neural coding properties. Cerebral Cortex, 11(12):1144-9. PDF
Abstract
The presence of a large number of inhibitory contacts at the soma and axon initial segment of cortical pyramidal cells has inspired a large and influential class of neural network model which use post-integration lateral inhibition as a mechanism for competition between nodes. However, inhibitory synapses also target the dendrites of pyramidal cells. The role of this dendritic inhibition in competition between neurons has not previously been addressed. We demonstrate, using a simple computational model, that such pre-integration lateral inhibition provides networks of neurons with useful representational and computational properties which are not provided by post-integration inhibition.


S. J. Grice, M. W. Spratling, A. Karmiloff-Smith, H. Halit, G. Csibra, M. de Haan and M. H. Johnson (2001) Disordered visual processing and oscillatory brain activity in autism and Williams Syndrome. NeuroReport, 12(12):2697-700. PDF
Abstract
Two developmental disorders, autism and Williams Syndrome, are both commonly described as having difficulties in integrating perceptual features, i.e., binding spatially separate elements into a whole. It is already known that healthy adults and infants display electroencephalographic (EEG) gamma band bursts (around 40Hz) when the brain is required to achieve such binding . Here we explore gamma band EEG in autism and Williams Syndrome and demonstrate differential abnormalities in the two phenotypes. We show that despite putative processing similarities at the cognitive level, binding in Williams Syndrome and autism can be dissociated at the neurophysiological level by different abnormalities in underlying brain oscillatory activity. Our study is the first to identify that binding related gamma EEG can be disordered in humans.


M. W. Spratling and M. H. Johnson (2001) Activity-dependent processes in regional cortical specialization [commentary]. Developmental Science, 4(2):153-4. HTML

G. Csibra, G. Davis, M. W. Spratling and M. H. Johnson (2000) Gamma oscillations and object processing in the infant brain. Science, 290(5496):1582-5. PDF
Abstract
An enduring controversy in neuroscience concerns how the brain binds together separately coded stimulus features to form unitary representations of objects. Recent evidence has indicated a close link between this binding process and 40Hz (gamma-band) oscillations generated by localized neural circuits (1). In a separate line of research, the ability of young infants to perceive objects as unitary and bounded has become a central focus for debates about the mechanisms of perceptual development (2). However, to date these infant studies have been behavioural, and there have been few, if any, paradigms involving direct measures of neural function. Here we demonstrate for the first time that binding-related 40Hz oscillations are evident in the infant brain around 8 months of age, the same age as some behavioral studies indicate the onset of perceptual binding of spatially separated static visual features. The discovery of binding-related gamma in infants opens up a new vista for experiments on postnatal functional brain development in infants.


M. W. Spratling and G. M. Hayes (2000) Learning synaptic clusters for non-linear dendritic processing. Neural Processing Letters, 11(1):17-27. PDFGzipped Postscript
Abstract
Nonlinear dendritic processing appears to be a feature of biological neurons and would also be of use in many applications of artificial neural networks. This paper presents a model of an initially standard linear unit which uses unsupervised learning to find clusters of inputs within which inactivity at one synapse can occlude the activity at the other synapses.


M. W. Spratling (1999) Pre-synaptic lateral inhibition provides a better architecture for self-organising neural networks. Network: Computation in Neural Systems, 10(4):285-301. PDFGzipped Postscript
Abstract
Unsupervised learning is an important property of the brain and of many artificial neural networks. A large variety of unsupervised learning algorithms have been proposed. This paper takes a different approach in considering the architecture of the neural network rather than the learning algorithm. It is shown that a self-organising neural network architecture using pre-synaptic lateral inhibition enables a single learning algorithm to find distributed, local, and topological representations as appropriate to the structure of the input data received. It is argued that such an architecture not only has computational advantages but is a better model of cortical self-organisation.

Other Refereed Publications

X. Zhang and M. W. Spratling (2008) Automated Learning of Coordinate Transformations. Proceedings of the Eighth International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (EPIROB08).

L. A. Watling, M. W. Spratling, K. De Meyer and M. Johnson (2007) The role of feedback in the determination of figure and ground: a combined behavioral and modeling study. Proceedings of the 29th Meeting of the Cognitive Science Society (COGSCI07). PDF
Abstract
Object knowledge can exert on important influence on even the earliest stages of visual processing. This study demonstrates how a familiarity bias, acquired only briefly before testing, can affect the subsequent segmentation of an otherwise ambiguous figure-ground array, in favor of perceiving the familiar shape as figure. The behavioral data are then replicated using a biologically plausible neural network model that employs feedback connections to implement the demonstrated familiarity bias.


M. W. Spratling and M. H. Johnson (2002) Exploring the functional significance of dendritic inhibition in cortical pyramidal cells. Proceedings of the 11th Computational Neuroscience Meeting (CNS02). (Reprinted in the journal Neurocomputing, 2003; see above)

M. W. Spratling (1999) Artificial Ontogenesis: A Connectionist Model of Development. PhD Thesis, University of Edinburgh. PDF
Abstract
This thesis suggests that ontogenetic adaptive processes are important for generating intelligent behaviour. It is thus proposed that such processes, as they occur in nature, need to be modelled and that such a model could be used for generating artificial intelligence, and specifically robotic intelligence. Hence, this thesis focuses on how mechanisms of intelligence are specified.

A major problem in robotics is the need to predefine the behaviour to be followed by the robot. This makes design intractable for all but the simplest tasks and results in controllers that are specific to that particular task and are brittle when faced with unforeseen circumstances. These problems can be resolved by providing the robot with the ability to adapt the rules it follows and to autonomously create new rules for controlling behaviour. This solution thus depends on the predefinition of how rules to control behaviour are to be learnt rather than the predefinition of rules for behaviour themselves.

Learning new rules for behaviour occurs during the developmental process in biology. Changes in the structure of the cerebral cortex underly behavioural and cognitive development throughout infancy and beyond. The uniformity of the neocortex suggests that there is significant computational uniformity across the cortex resulting from uniform mechanisms of development, and holds out the possibility of a general model of development. Development is an interactive process between genetic predefinition and environmental influences. This interactive process is constructive: qualitatively new behaviours are learnt by using simple abilities as a basis for learning more complex ones. The progressive increase in competence, provided by development, may be essential to make tractable the process of acquiring higher-level abilities.

While simple behaviours can be triggered by direct sensory cues, more complex behaviours require the use of more abstract representations. There is thus a need to find representations at the correct level of abstraction appropriate to controlling each ability. In addition, finding the correct level of abstraction makes tractable the task of associating sensory representations with motor actions. Hence, finding appropriate representations is important both for learning behaviours and for controlling behaviours. Representations can be found by recording regularities in the world or by discovering re-occurring patterns through repeated sensory-motor interactions. By recording regularities within the representations thus formed, more abstract representations can be found. Simple, non-abstract, representations thus provide the basis for learning more complex, abstract, representations.

A modular neural network architecture is presented as a basis for a model of development. The pattern of activity of the neurons in an individual network constitutes a representation of the input to that network. This representation is formed through a novel, unsupervised, learning algorithm which adjusts the synaptic weights to improve the representation of the input data. Representations are formed by neurons learning to respond to correlated sets of inputs. Neurons thus became feature detectors or pattern recognisers. Because the nodes respond to patterns of inputs they encode more abstract features of the input than are explicitly encoded in the input data itself. In this way simple representations provide the basis for learning more complex representations. The algorithm allows both more abstract representations to be formed by associating correlated, coincident, features together, and invariant representations to be formed by associating correlated, sequential, features together.

The algorithm robustly learns accurate and stable representations, in a format most appropriate to the structure of the input data received: it can represent both single and multiple input features in both the discrete and continuous domains, using either topologically or non-topologically organised nodes. The output of one neural network is used to provide inputs for other networks. The robustness of the algorithm enables each neural network to be implemented using an identical algorithm. This allows a modular `assembly' of neural networks to be used for learning more complex abilities: the output activations of a network can be used as the input to other networks which can then find representations of more abstract information within the same input data; and, by defining the output activations of neurons in certain networks to have behavioural consequences it is possible to learn sensory-motor associations, to enable sensory representations to be used to control behaviour.


M. W. Spratling and G. M. Hayes (1998) Learning sensory-motor cortical mappings without training. Proceedings of the 6th European Symposium on Artificial Neural Networks (ESANN98), M. Verleysen (ed.) pp. 339-44. D-facto Publications. Gzipped Postscript
Abstract
This paper shows how the relationship between two arrays of artificial neurons, representing different cortical regions, can be learned. The algorithm enables each neural network to self-organise into a topological map of the domain it represents at the same time as the relationship between these maps is found. Unlike previous methods learning is achieved without a separate training phase; the algorithm which learns the mapping is also that which performs the mapping.


M. W. Spratling and G. M. Hayes (1998) A self-organising neural network for modelling cortical development. Proceedings of the 6th European Symposium on Artificial Neural Networks (ESANN98), M. Verleysen (ed.) pp. 333-8. D-facto Publications. Gzipped Postscript
Abstract
This paper presents a novel self-organising neural network. It has been developed for use as a simplified model of cortical development. Unlike many other models of topological map formation all synaptic weights start at zero strength (so that synaptogenesis might be modelled). In addition, the algorithm works with the same format of encoding for both inputs to and outputs from the network (so that the transfer and recoding of information between cortical regions might be modelled).


M. W. Spratling (1997) Artificial Ontogenesis: Cognitive and Behavioural Development for Robots. Unpublished Departmental Discussion Paper, Department of Artificial Intelligence, University of Edinburgh.
Abstract
There are three classes of adaptive process (structural definition, structural adjustment, and parameter adjustment) which appear to underly the development of intelligence in nature. In artificial intelligence only two of these processes are used; AI ignores development (structural adjustment). While AI attempts to predefine explicit rules for behaviour, nature's success in building complex creatures depends on predefining how rules to control behaviour can be learned. It is the developmental processes in biology through which such rules are learned. This proposal is to apply mechanisms similar to those used in biological development to robots. This will move robotics from `development' meaning design and production, towards `development' in its biological sense meaning a process of growth and progressive change. Defining the rules for development is design at a meta-level to that currently used. It is proposed that the long process of evolution used by nature to define these developmental processes might be supplanted by another adaptive process, that of engineering, to more quickly enable study of ontogenetic development.

This project thus aims to apply techniques inspired by animal development to engineering robot control systems. Specifically it is proposed that a hierarchical control system, based on the cerebral cortex, is used and that this develops through constructivist learning algorithms (ones in which the interaction of a situated agent with its environment guides the creation of cognitive machinery appropriate for representing and acting in that environment). Such a robot would be provided with some innate, low-level, behavioural abilities and through experience develop more complex behaviour.


M. W. Spratling and R. Cipolla (1996) Uncalibrated visual servoing. Proceedings of the 7th British Machine Vision Conference (BMVC96), R. B. Fisher and E. Trucco (eds.) pp. 545-54. BMVA. Gzipped Postscript
Abstract
Visual servoing is a process to enable a robot to position a camera with respect to known landmarks using the visual data obtained by the camera itself to guide camera motion. A solution is described which requires very little a priori information freeing it from being specific to a particular configuration of robot and camera. The solution is based on closed loop control together with deliberate perturbations of the trajectory to provide calibration movements for refining that trajectory. Results from experiments in simulation and on a physical robot arm (camera-in-hand configuration) are presented.


M. W. Spratling (1994) Learning the Mapping Between Sensor and Motor Spaces to Produce Hand-Eye Coordination. MSc Thesis, Department of Artificial Intelligence, University of Edinburgh.
Abstract
Coordination between sensory inputs and motor actions is essential for intelligent robotics. This dissertation considers the control of a simple manipulator using sensory information to locate the target position for the end-effector. The control mechanisms investigated all form topographic maps of possible configurations of the manipulator joints (the motor space) and the values of the sensor inputs (the sensor space). Various methods are considered for learning to relate a location on the sensor space map (which represents the target position in the world) and the location in the motor space map which will configure the manipulator to reach this target position. These methods are analysed using a computer simulation and a suitable algorithm to solve the hand-eye coordination problem is presented.


M. M. Ross, M. W. Spratling, C. B. Kirkland and P. S. Story (1994) Measurement of microfog wetness in a model steam turbine using a miniature optical spectral extinction probe. IMechE International Symposium on Optical Methods and Data Processing In Heat and Fluid Flow.