The input strings consist of a timeordered sequenceof XY coordinates, punctuated by pen-lifts. The methods were designed to work in "run-on mode" where there is no constraint on the spacing between characters. While both methods use a neural network recognition engine and a graph-algorithmic post-processor, their approaches to segmentation are quite different. Thefirst method, which we call IN SEC (for input segmentation), usesa combination of heuristics to identify particular penlifts as tentative segmentation points. The second method, which we call OUTSEC (for output segmentation), relies on the empirically trainedrecognition engine for both recognizing characters and identifying relevant segmentation points. 1 INTRODUCTION We address the problem of writer independent recognition of hand-printed words from an 80,OOO-word English dictionary. Several levels of difficulty in the recognition of hand-printed words are illustrated in figure 1. The examples were extracted from our databases (table 1). Except in the cases of boxed or clearly spaced characters, segmenting characters independently of the recognition process yields poor recognition performance.This has motivated us to explore recognition-based segmentation techniques.

How can artificial neural nets generalize better from fewer examples? In order to generalize successfully, neural network learning methods typically require large training data sets. We introduce a neural network learning method that generalizes rationally from many fewer data points, relying instead on prior knowledge encoded in previously learned neural networks. For example, in robot control learning tasks reported here, previously learned networks that model the effects of robot actions are used to guide subsequent learning of robot control functions. For each observed training example of the target function (e.g. the robot control policy), the learner explains the observed example in terms of its prior knowledge, then analyzes this explanation to infer additional information about the shape, or slope, of the target function. This shape knowledge is used to bias generalization when learning the target function. Results are presented applying this approach to a simulated robot task based on reinforcement learning.

I shows a "spot plot" of the activity

It is known from biological data that the response patterns of interneurons in the olfactory macroglomerulus (MGC) of insects are of central importance for the coding of the olfactory signal. We propose an analytically tractable model of the MGC which allows us to relate the distribution of response patterns to the architecture of the network.

Electrophysiological studies (Cynader and Berman 1972, Robinson 1972) showed that the intermediate layer of SC is topographically organized into a motor map. The location of active neurons in this area was found to be related to the oculomotor error (Le.

This paper examines and extends the work of Linsker (1986) on self organising feature detectors. Linsker concentrates on the visual processingsystem, but infers that the weak assumptions made will allow the model to be used in the processing of other sensory information. This claim is examined here, with special attention paid to the auditory system, where there is much lower connectivity andtherefore more statistical variability. Online training is utilised, to obtain an idea of training times. These are then compared tothe time available to prenatal mammals for the formation of feature sensitive cells. 1 INTRODUCTION Within the last thirty years, a great deal of research has been carried out in an attempt to understand the development of cells in the pathways between the sensory apparatus and the cortex in mammals. For example, theories for the development of feature detectors were forwarded by Nass and Cooper (1975), by Grossberg (1976) and more recently Obermayer et al (1990). Hubel and Wiesel (1961) established the existence of several different types of feature sensitivecell in the visual cortex of cats. Various subsequent experiments have 1007 1008 Walton and Bisset shown that a considerable amount of development takes place before birth (i.e.

We present a local learning rule in which Hebbian learning is conditional on an incorrect prediction of a reinforcement signal. We propose a biological interpretation of such a framework and display its utility through examples in which the reinforcement signal is cast as the delivery of a neuromodulator to its target. Three exam pIes are presented which illustrate how this framework can be applied to the development of the oculomotor system. 1 INTRODUCTION Activity-dependent accounts of the self-organization of the vertebrate brain have relied ubiquitously on correlational (mainly Hebbian) rules to drive synaptic learning. Inthe brain, a major problem for any such unsupervised rule is that many different kinds of correlations exist at approximately the same time scales and each is effectively noise to the next. For example, relationships within and between the retinae among variables such as color, motion, and topography may mask one another and disrupt their appropriate segregation at the level of the thalamus or cortex.

We present a theory of cortico-hippocampal interaction in discrimination learning. The hippocampal region is presumed to form new stimulus representations which facilitate learning by enhancing the discriminability of predictive stimuli and compressing stimulus-stimulus redundancies. The cortical and cerebellar regions, which are the sites of long-term memory.

These are compared with single unit recordings and behavioural data. The firing of CAl place cells is simulated as the (artificial) rat moves in an environment. Thisis the input for a neuronal network whose output, at each theta (0) cycle, is the next direction of travel for the rat. Cells are characterised by the number of spikes fired and the time of firing with respect to hippocampal 0 rhythm. 'Learning' occurs in'on-off' synapses that are switched on by simultaneous pre-and post-synaptic activity.

Mechanisms of gastric rhythm generation in the isolated stomatogastric ganglion of spiny lobsters: Bursting pacemaker potentials, synaptic interactions and muscarinic modulation.