Information theory in neuroscience

Dimitrov, Alexander G.; Lazar, Aurel A.; Victor, Jonathan D.

doi:10.1007/s10827-011-0314-3

Cited by 101 publications

(74 citation statements)

References 76 publications

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“…Adami [31] has written a very readable account of the use of information concepts in evolutionary biology. Another field where information theory has found great success is neuroscience, where it is nowadays an essential tool; a good review is given by Dimitrov et al [32]. Such breadth of fields using information theory has led to competing definitions of information for different situations, including mutual information [4], directed information [33], Fisher information [34], and others.…”

Section: Information In An Evolutionary Contextmentioning

confidence: 99%

Noise, Information and Fitness in Changing Environments

Pedraza

Garcia

2018

Front. Phys.

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In this paper we discuss recent results regarding the question of adaptation to changing environments in intermediate timescales and the quantification of the amount of information a cell needs about its environment, connecting the theoretical approaches with relevant experimental results. We first show how advances in the study of noise in genetic circuits can inform a detailed description of intracellular information flow and allow for simplified descriptions of the phenotypic state of a cell. We then present the different types of strategies that cells can use to respond to changing environments, and what a quantitative description of this process implies about the long term fitness of the population. We present an early approach connecting the transmission of information to the average fitness, and then move on to a full model of the process. This model is then simplified to obtain analytical results for a few cases. We present the necessary notation but avoid technical detail as much as possible, as our goal is to emphasize the biological interpretation and significance of the mathematical results. We focus on how carefully constructed models can answer the long-standing objection to the use of information theory in biology based on decision-theoretic considerations of the difference between the amount of information and its fitness value.

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Section: Information In An Evolutionary Contextmentioning

confidence: 99%

Noise, Information and Fitness in Changing Environments

Pedraza

Garcia

2018

Front. Phys.

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“…Transfer entropy has been applied extensively in neuroscience [19][20][21][22] to uncover underlying network structures based solely on node behavior. Many other fields have used transfer entropy to infer network structure and/or relational dependencies between entities in complex networks: social networks [23,24], medicine [25], finance [26], climate [27,28], transportation [29], and biology [30,31], just to name a few.…”

Section: Transfer Entropy Applicationmentioning

confidence: 99%

Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

Rodewald

Colombi

Oyama

et al. 2016

Entropy

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Abstract:Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN) are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure's dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN's self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system's structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.

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“…The concept of information as a mathematical framework was first proposed in 1948 by the engineer and mathematician Claude Shannon [4] and was the birth to Information Theory [5]. It was originally intended for the analysis of telecommunication systems, but soon branched out into many other fields [6], including neuroscience [7].…”

Section: Introductionmentioning

confidence: 99%

“…Further, Attneave [11], Barlow [12] and later Shlens and colleagues [13,14] and Schneidman et al [15] used the information concept as a mutual constraint in studying the structure and function of the neural system [7]. At the same time, despite these and many other valuable contributions to neuroscience, information dynamics within the brain is not adequately captured by the classical description of information [3].…”

Section: Introductionmentioning

confidence: 99%

Information Flow in the Brain: Ordered Sequences of Metastable States

Fingelkurts

2017

Information

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Abstract:In this brief overview paper, we analyse information flow in the brain. Although Shannon's information concept, in its pure algebraic form, has made a number of valuable contributions to neuroscience, information dynamics within the brain is not fully captured by its classical description. These additional dynamics consist of self-organisation, interplay of stability/instability, timing of sequential processing, coordination of multiple sequential streams, circular causality between bottom-up and top-down operations, and information creation. Importantly, all of these processes are dynamic, hierarchically nested and correspond to continuous brain state change, even if the external environment remains constant. This is where metastable coordination comes into play. In a metastable regime of brain functioning, as a result of the simultaneous co-existence of tendencies for independence and cooperation, information is continuously created, preserved for some time and then dissipated through the formation of dynamical and nested spatio-temporal coalitions among simple neuronal assemblies and larger coupled conglomerates of them-so-called delocalised operational modules.

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Information theory in neuroscience

Cited by 101 publications

References 76 publications

Noise, Information and Fitness in Changing Environments

Noise, Information and Fitness in Changing Environments

Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

Information Flow in the Brain: Ordered Sequences of Metastable States

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