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Spikes: Exploring the Neural Code (Computational Neuroscience)
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Author: Fred Rieke, David Warland, Rob deRuytervanSteveninck, William Bialek
List Price: $30.00
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ISBN: 0262681080
Publisher: MIT Press (25 June, 1999)
Edition: Paperback
Sales Rank: 29,401
Average Customer Rating: 4.71 out of 5
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Customer ReviewsRating: 5 out of 5
Good book study for neural code
i looked this book, some difficults. but study neural code...
this book help you study neural code, and good friends...
Rating: 5 out of 5
a lot of interesting information
This is one of the best books on brain's neuronal system. Very self-contained, and without a lot of those overstatements you normally find in similar books. The basic points are discussed while many of the classical (but not very useful) points are ignored. The math is clear and the discussion of the real important question always very sharp.
Rating: 5 out of 5
Neuronal code -- it's all in the timing
Neural coding has traditionally been assumed to be one of rate coding, ie, the stronger the stimulus, then the more action potentials per second that a sensory neuron transmits, and so on throughout the nervous system. However, this book begins by pointing out that in various sensory systems there appears to be sparse temporal neural coding, ie, the timing of action potentials transmits information, and in fact does so quite efficiently. A mathematical basis is built up throughout the reference in order to support these claims. However, the general reader who has prior reading of other neurobiological references listed above and below, will nonetheless find the descriptive portions of this reference informative and reasonable to read. If a neuron can fire 100 spikes (ie, action potentials) per second, then it would appear that many biological phenomena are coded by no more than one or two spikes. For example, bat echolocation occurs on a time scale of 5-20 milliseconds (enough time for coding by a maximum of one or two spikes). For example, in the fly, movements across its visual field can cause it to generate a flight torque in less than 30 milliseconds (ie, enough time for only a few spikes). For example, in the rat hippocampus signaling about position is performed on the order of one or two spikes per neuron. The fact that single spikes are carrying information in these examples indicates that at least in some parts of the nervous system, a temporal neural coding exists. As well, the issue of neuron reliability is considered in detail. Traditionally, it has been considered that individual neurons are unreliable (for example, repeated presentations of the same sensory stimulus does not cause a sensory neuron to generate the same spike train each time), and that it is only in the context of the large network of neurons of the nervous system that perception is reliable (for example, an animal running through the woods at a high speed does not collide with trees). However, it is not so clear how the different spike trains generated each time by the sensory neuron in response to the same stimulus should really be quantified, and there is much evidence showing individual neurons to be quite reliable. For example, in human vision in very dim light individual photosensitive sensory neurons are detecting single photons. The fact that the many neural circuits after the photosensitive sensory neuron add little noise to the sensory neuron output, indicates that the neural computation involved must be very reliable. The fact that hyperacuity (ability to detect sensory stimuli beyond, albeit generally just somewhat beyond before it is truly impossible to do so, the threshold of physical reliability) exists also indicates the existence of a very reliable neural computation. For example, echolocating bats resolving jitter in the echoes on an order of 10 nanoseconds, or weakly electric fish resolving signal shifts on the order of 100s of nanoseconds, or human observers with a theoretical visual acuity threshold of 0.01 degree able to discriminate 0.002 degrees. Most of this reference analyzes single trains of spikes (ie, the action potentials being generated by a single neuron), and shows clearly that very few spikes can represent very precise computations. The last chapter of this book considers briefly more recent research on spike trains of multiple neurons.
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