## Information Theory and the BrainRoland Baddeley, Peter Hancock, Peter Földiák Information Theory and the Brain deals with a new and expanding area of neuroscience that provides a framework for understanding neuronal processing. This framework is derived from a conference held in Newquay, UK, where a group of scientists from around the world met to discuss the topic. This book begins with an introduction to the basic concepts of information theory and then illustrates these concepts with examples from research over the past forty years. Throughout the book, the contributors highlight current research from the areas of biological networks, information theory and artificial networks, information theory and psychology, and formal analysis. Each section includes an introduction and glossary covering basic concepts. |

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Information Theory and the Brain Roland Baddeley,Peter Hancock,Peter Földiák No preview available - 2000 |

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action potentials activity algorithm artiﬁcial Atick average Baddeley banana wavelets bandwidth biological bispectral bispectrum blowﬂy brain channel components computed conditional entropy correlation cortical cost deﬁned described distribution domain dominance stripes dynamics effect efﬁcient entorhinal cortex entropy equation estimate example Figure ﬁlters ﬁnd ﬁring rate ﬁrst ﬁxed Foldiak frequency function ganglion cells Gaussian hippocampus information density information theory information transmission input Laughlin learning maximise means measure minimise mutual information natural images neural network neurons noise object recognition output layer parameters pattern performance photoreceptors potential predicted processing proﬁle properties psychophysical pyramidal cells random REC network receptive ﬁeld redundancy reﬂect represent representation resource response retina retrieval Schaffer collaterals scotoma sensory sequence shown signal signiﬁcant simulations sparse coding spatial speciﬁc spike statistics stimulus sufﬁciently synapses temporal threshold tion transformation Treves units values vector VisNet vowel weight zero