Authored Books
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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
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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
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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.
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.
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.