By Mike Page (auth.), John A. Bullinaria BSc, MSc, PhD, David W. Glasspool BSc, Msc, George Houghton BA, MSc, PhD (eds.)
This quantity collects jointly refereed models of twenty-five papers offered on the 4th Neural Computation and Psychology Workshop, held at collage university London in April 1997. The "NCPW" workshop sequence is now good demonstrated as a full of life discussion board which brings jointly researchers from such assorted disciplines as man made intelligence, arithmetic, cognitive technological know-how, machine technology, neurobiology, philosophy and psychology to debate their paintings on connectionist modelling in psychology. the overall subject matter of this fourth workshop within the sequence used to be "Connectionist Repre sentations", an issue which not just attracted contributors from these types of fields, yet from allover the realm to boot. From the viewpoint of the convention organisers concentrating on representational matters had the virtue that it instantly concerned researchers from all branches of neural computation. Being so relevant either to psychology and to connectionist modelling, it really is one region approximately which each person within the box has their very own powerful perspectives, and the range and caliber of the shows and, simply as importantly, the dialogue which them, definitely attested to this.
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Extra resources for 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997: Connectionist Representations
Then, the weights, centres and angle values are updated using error back propagation so that the network converges quickly. 6. cx=:-----1 i=1 (' i : input nodes j : hidden nodes k : output nodes p : number of patterns cos CJ ,=0 for ail k Figure 6: Block diagram of CSFN for training where api = Xpi if unit i is an input unit, Cij are the centres for the RBF network, Wij are the weights in an MLP, W is the half opening angle, which can be any value in the range [-11"/2,11"/2] and determines the different forms of the decision borders, i and j are the indices referring to units in the input and the hidden layer and p refers to the number of the patterns.
REF networks pay attention to areas of input space detennined by their vectors. They also pay attention to individual dimensions in the input detennined by their nonn weights. The REF network is therefore able to both template or exemplar based accounts of categorization, and feature accounts. What sort of representations did the networks learn? centre space model based 24 A representation can be described as localist if there is a set of network parameters that can be interpreted as representing an object, at a suitable level of analysis.
So a zero might constitute evidence that the value of the greater-than relationship is false. In such cases the GSI of the task will be non-zero but still low. The general rule is that the more characteristically relational a problem is, the less significant are absolute values and the lower the geometric separability of the task is likely to be. Boundary methods tend to perform poorly on all relational problems. , the less significant absolute values are in the determination of output) the lower the GSI and the poorer the expected performance.