A more plausible biological version of the traditional perceptron is presented here with a learning rule which enables training of the neuron on nonlinear tasks. Three different models are introduced with varying inhibitory and excitatory synaptic connections. Using the derived learning rule, a single neuron is trained to successfully classify the XOR problem.

Selective visual attention in a natural environment can be seen as the interaction between the external visual stimulus and task specific knowledge of the required behavior. This interaction between the bottom-up stimulus and the top-down, task-related knowledge is crucial for what is selected in the space and time within the scene. In this paper, we propose a computational model for selective attention for a visual search task. We go beyond simple saliency-based attention models to model selective attention guided by top-down visual cues, which are dynamically integrated with the bottom-up information. In this way, selection of a location is accomplished by interaction between bottom-up and top-down information. First, the general structure of our model is briefly introduced and followed by a description of the top-down processing of task-relevant cues. This is then followed by a description of the processing of the external images to give three feature maps that are combined to give an overall bottom-up map. Second, the development of the formalism for our novel interactive spiking neural network (ISNN) is given, with the interactive activation rule that calculates the integration map. The learning rule for both bottom-up and top-down weight parameters are given, together with some further analysis of the properties of the resulting ISNN. Third, the model is applied to a face detection task to search for the location of a specific face that is cued. The results show that the trajectories of attention are dramatically changed by interaction of information and variations of cues, giving an appropriate, task-relevant search pattern. Finally, we discuss ways in which these results can be seen as compatible with existing psychological evidence.

For pt I see J. Phys. A, vol. 35, p. 2379-94 (2002).We develop neuron learning rules using the Informax principle together with the input-output relationship of the integrate-and-fire (IF) model with Poisson inputs. The learning rule is then tested with constant inputs, time-varying inputs and images. For constant inputs, it is found that, under the Informax principle, a network of IF models with initially all positive weights tends to disconnect some connections between neurons. For time-varying inputs and images, we perform signal separation tasks called independent component analysis. Numerical simulations indicate that some number of inhibitory inputs improves the performance of the system in both biological and engineering senses.

Transient synchronization has been used as a mechanism of recognizing auditory patterns using integrate-and-fire neural networks. We first extend the mechanism to vision tasks and investigate the role of spike dependent learning. We show that such a temporal Hebbian learning rule significantly improves accuracy of detection. We demonstrate how multiple patterns can be identified by a single pattern selective neuron and how a temporal album can be constructed. This principle may lead to multidimensional memories, where the capacity per neuron is considerably increased with accurate detection of spike synchronization.

While full computer understanding of dynamic visual scenes containing several people may be currently unattainable, we propose a computationally efficient approach to determine areas of interest in such scenes. We present methods for modelling and interpretation of multi-person human behaviour in real time to control video cameras for visually mediated interaction.

This paper reports initial research on supporting Visually Mediated In-teraction (VMI) by developing person-specific and generic gesture models for the control of active cameras. We describe a time-delay variant of the Radial Basis Function (TDRBF) network and evaluate its performance on recognising simple pointing and waving hand gestures in image sequences. Experimental results are presented that show that high levels of performance can be obtained for this type of gesture recognition using these techniques, both for particular individuals and across a set of individuals. Characteris-tic visual evidence can be automatically selected and used even to recognise individuals from their gestures, depending on the task demands. 1

The paper presents experiments using a radial basis function (RBF) network to tackle the unconstrained face recognition problem using low resolution video information. Input representations that mimic the effects of receptive field functions found at various stages of the human vision system were used with RBF network; that learnt to classify and generalise over different views of each person to be recognised. In particular, Difference of Gaussian (DoG) filtering and Gabor wavelet analysis are compared for face recognition from an image sequence. RBF techniques are shown to provide excellent levels of performance where the view varies and the authors discuss how to relax constraints on data capture and improve preprocessing to obtain an effective scheme for real-time, unconstrained face recognition.

This paper describes the problems and issues involved in developing artificial visual agents that watch moving objects and their interactions in real world situations. One objective of this work is to form conceptual descriptions that capture the behaviours of objects. To do this we use the dynamic scene context but here we go further and incorporate task context and simple learning of behavioural cues. The computational approach uses results from the VIEWS project. The issues concerned with extending computational vision to learn behavioural models and use attention are described for a surveillance system with "task-level control". This means that the visual processing is guided by both the current scene knowledge and the current surveillance task control policy.

In this work we address the issue of focused computation in computer vision for effectiveness and efficiency- In particular, we propose a scheme that links scene-oriented contextual knowledge with the computational constraints required in visual motion segmentation and tracking. The approach uses Ra.yesia.ii belief revision techniques to map explicit scene knowledge onto implicit causal dependent constraints in controlling computational parameters. We discuss our experimental results from applying this method in improving existing techniques in traffic surveillance applications. 1

This paper presents a path planning algorithm for mobile robots. We introduce a parallel search approach which is based on a regular grid representation of the map. The search is formulated as a cellular automaton by which local inter-cell communication rules are defined. The algorithm is made adaptive by utilising a multiresolution representation of the map. It is implemented on AMT DAP 510, which is a SIMD machine. I.