Connectionist models of development : developmental processes in real and artificial neural networks / edited by Philip T. Quinlan.

Includes bibliographical references and indexes.

Introduction : Modelling human development : in brief / Philip T. Quinlan -- A connectionist perspective on Piagetian development / Sylvain Sirois and Thomas R. Shultz -- Connectionist models of learning and development in infancy / Denis Mareschal -- The role of prefrontal cortex in perseveration : developmental and computational explorations / Yuko Munakata, J. Bruce Morton, and Jennifer Merva Stedron -- Language acquisition in a self-organising neural network model / Ping Li -- Connectionist modelling of lexical segmentation and vocabulary acquisition / Matt H. Davis -- Less is less in language acquisition / Douglas L.T. Rohde and David C. Plaut -- Pattern learning in infants and neural networks / Michael Gasser and Eliana Colunga -- Does visual development aid visual learning? / Melisssa Dominguez and Robert A. Jacobs -- Learning and brain development : a neural constructivist perspective / Steven R. Quartz -- Cross-modal neural development / Mark T. Wallace -- Evolutionary connectionism / Peter McLeod and Bodo Maass.

Print version record.

Connectionist Models of Development is an edited collection of essays on the current work concerning connectionist or neural network models of human development. The brain comprises millions of nerve cells that share myriad connections, and this book looks at how human development in these systems is typically characterised as adaptive changes to the strengths of these connections. The traditional accounts of connectionist learning, based on adaptive changes to weighted connections, are explored alongside the dynamic accounts in which networks generate their own structures as learnin.

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