On directed information theory and Granger causality graphs

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.Comment: accepted for publications, Journal of Computational Neuroscienc

Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation

Convex optimization of programmable quantum computers

A fundamental model of quantum computation is the programmable quantum gate array. This is a quantum processor that is fed by a program state that induces a corresponding quantum operation on input states. While being programmable, any finite-dimensional design of this model is known to be non-universal, meaning that the processor cannot perfectly simulate an arbitrary quantum channel over the input. Characterizing how close the simulation is and finding the optimal program state have been open questions for the past 20 years. Here, we answer these questions by showing that the search for the optimal program state is a convex optimization problem that can be solved via semi-definite programming and gradient-based methods commonly employed for machine learning. We apply this general result to different types of processors, from a shallow design based on quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits

The consumer scam: an agency-theoretic approach

Despite the extensive body of literature that aims to explain the phenomenon of consumer scams, the structure of information in scam relationships remains relatively understudied. The purpose of this article is to develop an agency-theoretical approach to the study of information in perpetrator-victim interactions. Drawing a distinction between failures of observation and failures of judgement in the pre-contract phase, we introduce a typology and a set of propositions that explain the severity of adverse selection problems in three classes of scam relationships. Our analysis provides a novel, systematic explanation of the structure of information that facilitates scam victimisation, while also enabling critical scrutiny of a core assumption in agency theory regarding contract design. We highlight the role of scam perpetrators as agents who have access to private information and exercise considerable control over the terms and design of scam relationships. Focusing on the consumer scam context, we question a theoretical assumption, largely taken for granted in the agency literature, that contact design is necessarily in the purview of the uninformed principal

Brief Exposure to Sensory Cues Elicits Stimulus-Nonspecific General Sensitization in an Insect

The effect of repeated exposure to sensory stimuli, with or without reward is well known to induce stimulus-specific modifications of behaviour, described as different forms of learning. In recent studies we showed that a brief single pre-exposure to the female-produced sex pheromone or even a predator sound can increase the behavioural and central nervous responses to this pheromone in males of the noctuid moth Spodoptera littoralis. To investigate if this increase in sensitivity might be restricted to the pheromone system or is a form of general sensitization, we studied here if a brief pre-exposure to stimuli of different modalities can reciprocally change behavioural and physiological responses to olfactory and gustatory stimuli. Olfactory and gustatory pre-exposure and subsequent behavioural tests were carried out to reveal possible intra- and cross-modal effects. Attraction to pheromone, monitored with a locomotion compensator, increased after exposure to olfactory and gustatory stimuli. Behavioural responses to sucrose, investigated using the proboscis extension reflex, increased equally after pre-exposure to olfactory and gustatory cues. Pheromone-specific neurons in the brain and antennal gustatory neurons did, however, not change their sensitivity after sucrose exposure. The observed intra- and reciprocal cross-modal effects of pre-exposure may represent a new form of stimulus-nonspecific general sensitization originating from modifications at higher sensory processing levels

Nonparametric signal detection with small type I and type II error probabilities

Signal detection, Nonparametric hypothesis testing, Kernel estimator, Large deviations,