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	<title>Neurobot &#187; Information Theory</title>
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	<link>http://neurobot.bio.auth.gr</link>
	<description>A computational neuroscience and neuroinformatics blog</description>
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		<title>Information Theory: A Tutorial Introduction</title>
		<link>http://neurobot.bio.auth.gr/2018/information-theory-a-tutorial-introduction/</link>
		<comments>http://neurobot.bio.auth.gr/2018/information-theory-a-tutorial-introduction/#comments</comments>
		<pubDate>Tue, 20 Feb 2018 15:39:29 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Information Theory]]></category>

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		<description><![CDATA[Shannon&#8217;s mathematical theory of communication defines fundamental limits on how much information can be transmitted between the different components of any man-made or biological system. This paper is an informal but rigorous introduction to the main ideas implicit in Shannon&#8217;s theory. An annotated reading list is provided for further reading. https://arxiv.org/pdf/1802.05968]]></description>
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		<title>An introductory review of information theory in the context of computational neuroscience</title>
		<link>http://neurobot.bio.auth.gr/2011/an-introductory-review-of-information-theory-in-the-context-of-computational-neuroscience/</link>
		<comments>http://neurobot.bio.auth.gr/2011/an-introductory-review-of-information-theory-in-the-context-of-computational-neuroscience/#comments</comments>
		<pubDate>Thu, 22 Sep 2011 16:20:56 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Information Theory]]></category>

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		<description><![CDATA[A nice review paper presenting introductory fundamental concepts of information theory, from engineering to neuroscience. The paper was published as: McDonnell MD, Ikeda S and Manton JH, &#8221;An introductory review of information theory in the context of computational neuroscience&#8220;, Biological Cybernetics 2011;105(1):55-70. Also available in PDF format here.]]></description>
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		<title>Information theory methods in Neuroscience</title>
		<link>http://neurobot.bio.auth.gr/2011/information-theory-methods-in-neuroscience/</link>
		<comments>http://neurobot.bio.auth.gr/2011/information-theory-methods-in-neuroscience/#comments</comments>
		<pubDate>Wed, 27 Apr 2011 09:41:41 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Information Theory]]></category>
		<category><![CDATA[Neurophysiology]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2913</guid>
		<description><![CDATA[Back in 1948, Shannon introduced Information Theory with his landmark paper &#8220;A mathematical theory of communication&#8221;[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 [...]]]></description>
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		<title>Signal-to-noise ratio in neuroscience</title>
		<link>http://neurobot.bio.auth.gr/2009/signal-to-noise-ratio-in-neuroscience/</link>
		<comments>http://neurobot.bio.auth.gr/2009/signal-to-noise-ratio-in-neuroscience/#comments</comments>
		<pubDate>Sat, 05 Dec 2009 20:53:27 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Information Theory]]></category>
		<category><![CDATA[Neurophysiology]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2379</guid>
		<description><![CDATA[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. A common use of SNR is to compare the [...]]]></description>
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		<title>Spike train analysis toolkit</title>
		<link>http://neurobot.bio.auth.gr/2008/spike-train-analysis-toolkit/</link>
		<comments>http://neurobot.bio.auth.gr/2008/spike-train-analysis-toolkit/#comments</comments>
		<pubDate>Thu, 28 Feb 2008 14:22:33 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Information Theory]]></category>

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		<description><![CDATA[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. This implementation behaves like a typical Matlab toolbox, [...]]]></description>
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		<title>How Behavioral Constraints May Determine Optimal Sensory Representations</title>
		<link>http://neurobot.bio.auth.gr/2007/how-behavioral-constraints-may-determine-optimal-sensory-representations/</link>
		<comments>http://neurobot.bio.auth.gr/2007/how-behavioral-constraints-may-determine-optimal-sensory-representations/#comments</comments>
		<pubDate>Fri, 16 Mar 2007 09:56:13 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Information Theory]]></category>

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		<description><![CDATA[&#8220;The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron&#8217;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?&#8221; by Emilio Salinas Department of Neurobiology and Anatomy, Wake Forest University School [...]]]></description>
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		<title>Information theory and neural coding</title>
		<link>http://neurobot.bio.auth.gr/2006/information-theory-and-neural-coding/</link>
		<comments>http://neurobot.bio.auth.gr/2006/information-theory-and-neural-coding/#comments</comments>
		<pubDate>Wed, 26 Apr 2006 13:04:27 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Information Theory]]></category>

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		<description><![CDATA[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 [...]]]></description>
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		<title>Entropy and Information in Neural Spike Trains</title>
		<link>http://neurobot.bio.auth.gr/2006/entropy-and-information-in-neural-spike-trains/</link>
		<comments>http://neurobot.bio.auth.gr/2006/entropy-and-information-in-neural-spike-trains/#comments</comments>
		<pubDate>Thu, 23 Mar 2006 20:04:51 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Information Theory]]></category>

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		<description><![CDATA[The nervous system represents time-dependent signals in sequences of discrete action potentials or spikes, all spikes are identical so that information is carried only in the spike arrival times. A scientific paper by Steven P. Strong, Roland Koberle, Rob R. de Ruyter van Steveninck, and William Bialek -NEC Research Institute, Princeton, New Jersey -Department of [...]]]></description>
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		<slash:comments>0</slash:comments>
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		<title>Quantifying the information transmitted in a single stimulus</title>
		<link>http://neurobot.bio.auth.gr/2006/quantifying-the-information-transmitted-in-a-single-stimulus/</link>
		<comments>http://neurobot.bio.auth.gr/2006/quantifying-the-information-transmitted-in-a-single-stimulus/#comments</comments>
		<pubDate>Sun, 19 Mar 2006 17:51:14 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Information Theory]]></category>

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		<description><![CDATA[Shannon mutual information provides a measure of how much information is, on average, contained in a set of neural activities about a set of stimuli. It has been extensively used to study neural coding in different brain areas. To apply a similar approach to investigate single stimulus encoding, the authors need to introduce a quantity [...]]]></description>
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