4th Neural Computation and Psychology Workshop, London, 9–11 by Mike Page (auth.), John A. Bullinaria BSc, MSc, PhD, David

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 awarded on the 4th Neural Computation and Psychology Workshop, held at college collage London in April 1997. The "NCPW" workshop sequence is now good tested as a full of life discussion board which brings jointly researchers from such varied disciplines as man made intelligence, arithmetic, cognitive technology, laptop technological know-how, neurobiology, philosophy and psychology to debate their paintings on connectionist modelling in psychology. the final subject of this fourth workshop within the sequence used to be "Connectionist Repre­ sentations", an issue which not just attracted contributors from most of these fields, yet from allover the area besides. From the perspective of the convention organisers targeting representational matters had the virtue that it instantly concerned researchers from all branches of neural computation. Being so primary either to psychology and to connectionist modelling, it's one quarter approximately which all people within the box has their very own powerful perspectives, and the variety and caliber of the shows and, simply as importantly, the dialogue which them, definitely attested to this.

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Additional resources for 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997: Connectionist Representations

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The main features of this particular model are: (a) The input set is comprised of a number of N-dimension vectors XP E R n , P) (X P)t -- (x Pl' x P2' •••'xn ' (1) where p indexes the occurrence of X in a training set; (b) the mathematical definition of the one-hidden layer network that has to be created for recognising all its members has the following form: I Ci = O'(~)Wik hk)) (2) j=o where n hk = O'(L: (Wkj x~)) (3) j=O corresponds to the output of a generic hidden unit hk of the architecture; represents the activation function employed, which subsequently will be considered to be the sigmoid unless otherwise stated; 0' 49 (c) the network produces as output a class index C = (Cl,··,Cj,··,cm ), Ci E {O, I} and C E (.

0, each REF pays attention to all input dimensions, and the network therefore perfonns template matching. Finally, the centres and the nonn weights were all adapted as were the weights and the network learned the following structure, Figure 8. • • • • -11 2 Figure 8. 030). The network has again extracted two of the prototypes as centre vectors, but now ignores some of the inputs. In particular, two of the inputs (7 and 9) in centre 1 are ignored so that this REF acts as a feature detector rather than a template matcher.

Phase B (error back propagation phase): Step Bl : The error signal vectors /5 , of both layers are calculated. Step B2 : Output layer weights are adjusted by the back propagation algorithm. Step B3 : Hidden layer weights, centres and opening angle ware updated. Step B4 : The outputs of the layers are calculated. Step B5 : New SSE is computed. If this is larger than the error goal, go to step Bl, otherwise, terminate the training session. 3 Methods For Training Two different algorithms for training the CSFN are proposed in this work.

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