Finite-State Channel Models for Signal Transduction in Neural Systems

Information theory provides powerful tools for understanding communication systems. This analysis can be applied to intercellular signal transduction, which is a means of chemical communication among cells and microbes. We discuss how to apply information-theoretic analysis to ligand-receptor systems, which form the signal carrier and receiver in intercellular signal transduction channels. We also discuss the applications of these results to neuroscience.Comment: Accepted for publication in 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing, Shanghai, Chin

Information Theory and Noisy Computation

We report on two types of results. The first is a study of the rate of decay of information carried by a signal which is being propagated over a noisy channel. The second is a series of lower bounds on the depth, size, and component reliability of noisy logic circuits which are required to compute some function reliably. The arguments used for the circuit results are information-theoretic, and in particular, the signal decay result is essential to the depth lower bound. Our first result can be viewed as a quantified version of the data processing lemma, for the case of Boolean random variables

Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information

In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.Comment: Submitted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP

An information theoretic perspective on multimodal signal processing

Multimodal signals can be defined in general as signals originating from the same physical source, but acquired through different devices, techniques or protocols. This applies for example to audio-visual signals, medical or satellite images. Understanding the joint dependencies of such signals is the first step toward intelligent means for their analysis. Information theory offers a rich theoretical framework in which such dependencies can be emphasized and from this, new methods of signal analysis can be developed

The role of Signal Processing in Meeting Privacy Challenges [an overview]

International audienceWith the increasing growth and sophistication of information technology, personal information is easily accessible electronically. This flood of released personal data raises important privacy concerns. However, electronic data sources exist to be used and have tremendous value (utility) to their users and collectors, leading to a tension between privacy and utility. This article aims to quantify that tension by means of an information-theoretic framework and motivate signal processing approaches to privacy problems. The framework is applied to a number of case studies to illustrate concretely how signal processing can be harnessed to provide data privacy

Efficient Information Theoretic Clustering on Discrete Lattices

We consider the problem of clustering data that reside on discrete, low dimensional lattices. Canonical examples for this setting are found in image segmentation and key point extraction. Our solution is based on a recent approach to information theoretic clustering where clusters result from an iterative procedure that minimizes a divergence measure. We replace costly processing steps in the original algorithm by means of convolutions. These allow for highly efficient implementations and thus significantly reduce runtime. This paper therefore bridges a gap between machine learning and signal processing.Comment: This paper has been presented at the workshop LWA 201