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The information bottleneck method
TLDR
We define the relevant information in a signal $x\in X$ as being the information that this signal provides about another signal $y\in \Y$. Expand
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Spikes: Exploring the Neural Code
TLDR
The authors invite the reader to play the role of a hypothetical observer inside the brain who makes decisions based on the incoming spike trains. Expand
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Weak pairwise correlations imply strongly correlated network states in a neural population
Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint thatExpand
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Entropy and Information in Neural Spike Trains
TLDR
The nervous system represents time dependent signals in sequences of discrete, identical action potentials or spikes; information is carried only in the spike arrival times. Expand
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Probing the Limits to Positional Information
The reproducibility and precision of biological patterning is limited by the accuracy with which concentration profiles of morphogen molecules can be established and read out by their targets. WeExpand
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Stability and Nuclear Dynamics of the Bicoid Morphogen Gradient
Patterning in multicellular organisms results from spatial gradients in morphogen concentration, but the dynamics of these gradients remain largely unexplored. We characterize, through in vivoExpand
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Statistics of Natural Images: Scaling in the Woods
TLDR
In order to best understand a visual system one should attempt to characterize the natural images it processes. Expand
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Adaptive Rescaling Maximizes Information Transmission
TLDR
Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions. Expand
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Efficiency and ambiguity in an adaptive neural code
TLDR
We examine the dynamics of a neural code in the context of stimuli whose statistical properties are themselves evolving dynamically. Expand
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Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions
TLDR
We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli that are nongaussian and exhibit strong correlations. Expand
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