Neutral theory and scale-free neural dynamics

Avalanches of electrochemical activity in brain networks have been empirically reported to obey scale-invariant behavior --characterized by power-law distributions up to some upper cut-off-- both in vitro and in vivo. Elucidating whether such scaling laws stem from the underlying neural dynamics operating at the edge of a phase transition is a fascinating possibility, as systems poised at criticality have been argued to exhibit a number of important functional advantages. Here we employ a well-known model for neural dynamics with synaptic plasticity, to elucidate an alternative scenario in which neuronal avalanches can coexist, overlapping in time, but still remaining scale-free. Remarkably their scale-invariance does not stem from underlying criticality nor self-organization at the edge of a continuous phase transition. Instead, it emerges from the fact that perturbations to the system exhibit a neutral drift --guided by demographic fluctuations-- with respect to endogenous spontaneous activity. Such a neutral dynamics --similar to the one in neutral theories of population genetics-- implies marginal propagation of activity, characterized by power-law distributed causal avalanches. Importantly, our results underline the importance of considering causal information --on which neuron triggers the firing of which-- to properly estimate the statistics of avalanches of neural activity. We discuss the implications of these findings both in modeling and to elucidate experimental observations, as well as its possible consequences for actual neural dynamics and information processing in actual neural networks.Comment: Main text: 8 pages, 3 figures. Supplementary information: 5 pages, 4 figure

Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX~2

On the Role of Primary and Secondary Assets in Adaptive Security: An Application in Smart Grids

peer-reviewedAdaptive security aims to protect valuable assets managed by a system, by applying a varying set of security controls. Engineering adaptive security is not an easy task. A set of effective security countermeasures should be identified. These countermeasures should not only be applied to (primary) assets that customers desire to protect, but also to other (secondary) assets that can be exploited by attackers to harm the primary assets. Another challenge arises when assets vary dynamically at runtime. To accommodate these variabilities, it is necessary to monitor changes in assets, and apply the most appropriate countermeasures at runtime. The paper provides three main contributions for engineering adaptive security. First, it proposes a modeling notation to represent primary and secondary assets, along with their variability. Second, it describes how to use the extended models in engineering security requirements and designing required monitoring functions. Third, the paper illustrates our approach through a set of adaptive security scenarios in the customer domain of a smart grid. We suggest that modeling secondary assets aids the deployment of countermeasures, and, in combination with a representation of assets variability, facilitates the design of monitoring function