Optimal, unsupervised learning in invariant object recognition

A means for establishing transformation-invariant representations of objects is proposed and analyzed, in which different views are associated on the basis of the temporal order of the presentation of these views, as well as their spatial similarity. Assuming knowledge of the distribution of presentation times, an optimal linear learning rule is derived. Simulations of a competitive network trained on a character recognition task are then used to highlight the success of this learning rule in relation to simple Hebbian learning and to show that the theory can give accurate quantitative predictions for the optimal parameters for such networks

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus

Face processing : the role of dynamic information

This thesis explores the effects of movement on various face processing tasks. In Experiments One to Four, unfamiliar face recognition was investigated using identical numbers of frames in the learning phase; these were viewed as a series of static images, or in moving sequences (using computer animation). There was no additional benefit from studying the moving sequences, but signal detection measurements showed an advantage for using dynamic sequences at test. In Experiments Five and Six, moving and static images of unfamiliar faces were matched for expression or identity. Without prior study, movement only helped in matching the expression. It was proposed that motion provided more effective access to a stored representation of an emotional expression. Brief familiarisation with the faces led to an advantage for dynamic presentations in referring to a stored representation of identity as well as expression. Experiments Seven to Nine explored the suggestion that motion is beneficial when accessinga pre-existingd escription. Significantly more famous faces were recognised in inverted and negated formats when shown in dynamic clips, compared with recognition using static images. This benefit may be through detecting idiosyncratic gesture patterns at test, or extracting spatial and temporal relationships which overlapped the stored kinematic details. Finally, unfamiliar faces were studied as moving or static images; recognition was tested under dynamic or fixed conditions using inverted or negated formats. As there was no difference between moving and static study phases, it was unlikely that idiosyncratic gesture patterns were being detected, so the significant advantage for motion at test seemed due to an overlap with the stored description. However, complex interactions were found, and participants demonstrated bias when viewing motion at test. Future work utilising dynamic image-manipulated displays needs to be undertaken before we fully understand the processing of facial movement

Artificial ontogenesis: a connectionist model of development

This thesis suggests that ontogenetic adaptive processes are important for generating intelligent beha- viour. 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 abstrac- tion 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 pat- terns 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 pat- tern 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 pat- tern 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 represent- ations 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

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

Optimal, Unsupervised Learning in Invariant Object Recognition

this paper to build a theoretical framework for analyzing the response of a neuron over time and to derive the exact form of an optimal training rule, itself determined solely by the temporal structure of the world to which the neurons are exposed. 2 Mapping the output of a neuron to the optimal training signa