Latent Inhibition and Its Neural Substrates describes a neural network model of attentional processes during associative learning, mainly latent inhibition, and shows how variables in the model can be mapped onto different brain regions and neurotransmitters. The result is a neurophysiological model capable of generating predictions and descriptions of numerous experimental results using latent inhibition, including the effects of brain lesions, drug administration, and the combination of both. The model also explains the absence of latent inhibition in acute schizophrenia and its reinstatement by the administration of psychotropic drugs.
In this advanced text, the author, starting with the simple assumption that psychological associations are represented by the strength of synaptic connections, details several mechanistic descriptions of complex cognitive behaviors. Part I presents neural network theories of classical conditioning; Part II describes neural networks of operant conditioning, and animal communication; Part III discusses spatial and cognitive mapping, and finally, Part IV shows how neural network models permit one to simultaneously develop psychological theories and models of the brain. The book includes computer software that allows the computer simulation of classical conditioning and the effect of different brain lesions on many classical paradigms. All those people interested in neural networks, from psychologists, through neuroscientists to computer scientists working on artificial intelligence and robotics, will find this book an excellent advanced guide to the subject.
Latent Inhibition and Its Neural Substrates describes a neural network model of attentional processes during associative learning, mainly latent inhibition, and shows how variables in the model can be mapped onto different brain regions and neurotransmitters. The result is a neurophysiological model capable of generating predictions and descriptions of numerous experimental results using latent inhibition, including the effects of brain lesions, drug administration, and the combination of both. The model also explains the absence of latent inhibition in acute schizophrenia and its reinstatement by the administration of psychotropic drugs.
What mechanisms are involved in enabling us to generate predictions of what will happen in the near future? Although we use associative mechanisms as the basis to predict future events, such as using cues from our surrounding environment, timing, attentional, and configural mechanisms are also needed to improve this function. Timing mechanisms allow us to determine when those events will take place. Attentional mechanisms ensure that we keep track of cues that are present when unexpected events occur and disregard cues present when everything happens according to our expectations. Configural mechanisms make it possible to combine separate cues into one signal that predicts an event different from that predicted individually by separate cues. Written for graduates and researchers in neuroscience, computer science, biomedical engineering and psychology, the author presents neural network models that incorporate these mechanisms and shows, through computer simulations, how they explain the multiple properties of associative learning"--Provided by publisher.
In this advanced text, the author, starting with the simple assumption that psychological associations are represented by the strength of synaptic connections, details several mechanistic descriptions of complex cognitive behaviors. Part I presents neural network theories of classical conditioning; Part II describes neural networks of operant conditioning, and animal communication; Part III discusses spatial and cognitive mapping, and finally, Part IV shows how neural network models permit one to simultaneously develop psychological theories and models of the brain. The book includes computer software that allows the computer simulation of classical conditioning and the effect of different brain lesions on many classical paradigms. All those people interested in neural networks, from psychologists, through neuroscientists to computer scientists working on artificial intelligence and robotics, will find this book an excellent advanced guide to the subject.
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