Neurodynamics and Psychology
The recent resurgence in neural network and connectionist approaches to modeling cognitive phenomena has led to a strong backlash from the classical cognitive psychology framework. Neurodynamics and Psychologypresents a collection of recent work that explores the connectionist versus the classical debate from a variety of perspectives, putting forward the case that the study of neurodynamics may represent the only way forward in the search for solutions to recently emerging problems for neural network models across a wide range of diverse research areas.
The book is divided into three parts: Part I covers the general area of attention and action, Part II examines the psychobiology of time and the role of temporal mechanisms in providing a solution to the psychologically important binding problem, and Part III reports on the computational modeling of the dynamic psychological processing involved in language and memory.
Neurodynamics and Psychology is of great interest to researchers in the areas of cognitive psychology, cognitive neuroscience, neural networks and neuropsychology.
* The recent resurgence in neural network and connectionist approaches to modelling cognitive phenomena has led to a backlash from the classical cognitive psychology framework. The purpose of this book is to present recent work which explores the connectionist v classical debate, and produces a balanced view of work in the area.
* The book argues that neurodynamics may represent the only way forward in the search for solutions to recently-emerging problems across a wide range of apparently idverse research areas.
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List of Contributors
Neuronal Models of Cognitive Functions Associated with
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algorithm approach architecture areas attention attractor back-propagation basal ganglia behaviour binding brain cells Changeux clusters cognitive competitor unit complex computational connectionist connections context copy-back cortical deficits Dehaene described discussed disruption dissipative structures distractor dynamical systems Elman encoding episodic memories error example excitatory external input feedback feedforward Figure forward model frontal frontal lobe function hidden units implementation inhibition inhibitory input units involved language lateral inhibition layer learning limit cycles linguistic loop mechanism memory motor neurons node Noun opponent circuit opponent process organisation oscillator output parameters particular pattern performance phoneme positive possible prediction prefrontal cortex problem produce properties psychological recurrent representation represented role rule rule-coding Rumelhart scalar schema schemata schizophrenia selection sequence sequential similar simulations spatial statistical stimulus structure suggest symptoms synapses synchronisation syntactic categories target task temporal thalamic theory time-dependent time-step variable words