The information bottleneck method
The variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.
Spikes: Exploring the Neural Code
Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory about the representation of sensory signals in neural spike trains and a quantitative framework is used to pose precise questions about the structure of the neural code.
Weak pairwise correlations imply strongly correlated network states in a neural population
It is shown, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons, and it is found that this collective behaviour is described quantitatively by models that capture the observed pairwise correlations but assume no higher-order interactions.
Entropy and Information in Neural Spike Trains
It is shown how to quantify this information, in bits, free from any assumptions about which features of the spike train or input signal are most important, and this approach is applied to the analysis of experiments on a motion sensitive neuron in the fly visual system.
Probing the Limits to Positional Information
Statistics of Natural Images: Scaling in the Woods
This work gathers images from the woods and finds that these scenes possess an ensemble scale invariance, and this non-Gaussian character cannot be removed through local linear filtering, meaning information is maximized at fixed channel variance.
Stability and Nuclear Dynamics of the Bicoid Morphogen Gradient
Efficiency and ambiguity in an adaptive neural code
The dynamics of a neural code is examined in the context of stimuli whose statistical properties are themselves evolving dynamically, thus resolving potential ambiguities and approaching the physical limit imposed by statistical sampling and noise.
Adaptive Rescaling Maximizes Information Transmission
Reading a Neural Code
Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train, applied to an identified movement-sensitive neuron in the fly visual system.