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 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.
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.
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.
Both direct photobleaching measurements and indirect estimates of Bicoid-eGFP diffusion constants provide a consistent picture of BICoid transport on short time scales but challenge traditional models of long-range gradient formation.
This agreement among different measures of accuracy indicates that the Drosophila embryo is not faced with noisy input signals and readout mechanisms; rather, the system exerts precise control over absolute concentrations and responds reliably to small concentration differences, approaching the limits set by basic physical principles.
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.
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.
This work relates an adaptive property of a sensory system directly to its function as a carrier of information about input signals, and gives direct evidence that the scaling of the input/output relation is set to maximize information transmission for each distribution of signals.
A method that allows for a rigorous statistical analysis of neural responses to natural stimuli that are nongaussian and exhibit strong correlations is proposed, which maximize the mutual information between the neural responses and projections of the stimulus onto low-dimensional subspaces.