Wireless Network Control with Privacy Using Hybrid ARQ

We consider the problem of resource allocation in a wireless cellular network, in which nodes have both open and private information to be transmitted to the base station over block fading uplink channels. We develop a cross-layer solution, based on hybrid ARQ transmission with incremental redundancy. We provide a scheme that combines power control, flow control, and scheduling in order to maximize a global utility function, subject to the stability of the data queues, an average power constraint, and a constraint on the privacy outage probability. Our scheme is based on the assumption that each node has an estimate of its uplink channel gain at each block, while only the distribution of the cross channel gains is available. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable utility given the available channel state information

Adaptive Smoothing in fMRI Data Processing Neural Networks

Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines into end-to-end learning networks. This change of paradigm offers new avenues to improvement as it allows for a global optimisation. The current work aims at benefitting from this paradigm shift by defining a smoothing step as a layer in these networks able to adaptively modulate the degree of smoothing required by each brain volume to better accomplish a given data analysis task. The viability is evaluated on real fMRI data where subjects did alternate between left and right finger tapping tasks.Comment: 4 pages, 3 figures, 1 table, IEEE 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI

Bursts generate a non-reducible spike pattern code

At the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. By using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the what and the when of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems