Life depends as much on the flow of information as on the flow of energy. Here we review
the many efforts to make this intuition precise. Starting with the building blocks of information …

We consider the problem of estimating Shannon's entropy H from discrete data, in cases
where the number of possible symbols is unknown or even countably infinite. The Pitman …

This paper proposes a random forests-based method appropriate for detecting small objects
such as Unmanned Aerial Vehicles (UAVs) and aircrafts when these objects occupy a small …

Entropy estimation is a fundamental problem in information theory that has applications in
various fields, including physics, biology, and computer science. Estimating the entropy of …

Abstract Theory suggest that networks of neurons may predict their input. Prediction may
underlie most aspects of information processing and is believed to be involved in motor and …

Retina is a paradigmatic system for studying sensory encoding: the transformation of light
into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed …

Neural responses are variable: even under identical experimental conditions, single neuron
and population responses typically differ from trial to trial and across time. Recent work has …

To compensate for sensory processing delays, the visual system must make predictions to
ensure timely and appropriate behaviors. Recent work has found predictive information …

We investigate the memory properties of discrete sequences built upon a finite number of
states. We find that the block entropy can reliably determine the memory for systems …

Entropy makes it possible to measure the uncertainty about an information source from the
distribution of its output symbols. It is known that the maximum Shannon's entropy of a …