A nice review paper presenting introductory fundamental concepts of information theory, from engineering to neuroscience.

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Back in 1948, Shannon introduced Information Theory with his landmark paper “A mathematical theory of communication”[1]. Since then, Neuroscience has been treated by researchers as a major field of application of this new theory,  beyond its original scope. A recent special issue of the Journal of Computational Neuroscience on Methods of Information Theory in Neuroscience Research focuses on current methods and applications of Information Theory to neural systems. Read the rest of this entry…

By Simon R Schultz (2007), Scholarpedia, 2(6):2046
Signal-to-Noise ratio (SNR) generically means the dimensionless ratio of signal power to noise power. It has a long history of being used in neuroscience as a measure of the fidelity of signal transmission and detection by neurons and synapses.

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Information theoretic methods are now widely used for the analysis of spike train data. However, developing robust implementations of these methods can be tedious and time-consuming. In order to facilitate further adoption of these methods, the Spike Train Analysis Toolkit implements several information-theoretic spike train analysis techniques.

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“The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron’s average response as a function of a parameter that characterizes a physical stimulus. What determines the shapes of tuning curves in a neuronal population?”

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Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus-response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis.

The authors review information-theory basics before demonstrating its use in neural coding, validating simple stimulus-response models of neural coding of dynamic stimuli.
By Alexander Borst & Frederic E. Theunissen
Nature Neuroscience 2, 947 – 957 (1999)
doi:10.1038/14731

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