TACOP: A Cognitive Agent for a Naval Training Simulation Environment

The full version of this paper appeared in: Doesburg, W. A. van, Heuvelink, A., and Broek, E. L. van den (2005). TACOP: A cognitive agent for a naval training simulation environment. In M. Pechoucek, D. Steiner, and S. Thompson (Eds.), Proceedings of the Industry Track of the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-05), p.34-41. July 25-29, Utrecht, The Netherlands

Enhancing human understanding through intelligent explanations,”

Abstract. Ambient systems that explain their actions promote the user's understanding as they give the user more insight in the effects of their behavior on the environment. In order to provide individualized intelligent explanations, we need not only to evaluate a user's observable behavior, but we also need to make sense of the underlying beliefs, intentions and strategies. In this paper we argue for the need of intelligent explanations, identify the requirements of such explanations, propose a method to achieve generation of intelligent explanations, and report on a prototype in the training of naval situation assessment and decision making. We discuss the implications of intelligent explanations in training and set the agenda for future research

Activity and Process Stability of Purified Green Pepper (Capsicum annuum) Pectin Methylesterase

Pectin methylesterase (PME) from green bell peppers (Capsicum annuum) was extracted and purified by affinity chromatography on a CNBr-Sepharose-PMEI column. A single protein peak with pectin methylesterase activity was observed. For the pepper PME, a biochemical characterization in terms of molar mass (MM), isoelectric points (pI), and kinetic parameters for activity and thermostability was performed. The optimum pH for PME activity at 22 °C was 7.5, and its optimum temperature at neutral pH was between 52.5 and 55.0 °C. The purified pepper PME required the presence of 0.13 M NaCl for optimum activity. Isothermal inactivation of purified pepper PME in 20 mM Tris buffer (pH 7.5) could be described by a fractional conversion model for lower temperatures (55?57 °C) and a biphasic model for higher temperatures (58?70 °C). The enzyme showed a stable behavior toward high-pressure/temperature treatments. Keywords: Capsicum annuum; pepper; pectin methylesterase; purification; characterization; thermal and high-pressure stabilit

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fninf. 2017.00007/full#supplementary-materialModeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.This study was supported by the European Union NR (658479-Spike Control), the Spanish National Grant NEUROPACT (TIN2013-47069-P) and by the Spanish National Grant PhD scholarship (AP2012-0906). We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan GPUs for current EDLUT development

Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences

Stochastic Resonance Modulates Neural Synchronization within and between Cortical Sources

Neural synchronization is a mechanism whereby functionally specific brain regions establish transient networks for perception, cognition, and action. Direct addition of weak noise (fast random fluctuations) to various neural systems enhances synchronization through the mechanism of stochastic resonance (SR). Moreover, SR also occurs in human perception, cognition, and action. Perception, cognition, and action are closely correlated with, and may depend upon, synchronized oscillations within specialized brain networks. We tested the hypothesis that SR-mediated neural synchronization occurs within and between functionally relevant brain areas and thus could be responsible for behavioral SR. We measured the 40-Hz transient response of the human auditory cortex to brief pure tones. This response arises when the ongoing, random-phase, 40-Hz activity of a group of tuned neurons in the auditory cortex becomes synchronized in response to the onset of an above-threshold sound at its “preferred” frequency. We presented a stream of near-threshold standard sounds in various levels of added broadband noise and measured subjects' 40-Hz response to the standards in a deviant-detection paradigm using high-density EEG. We used independent component analysis and dipole fitting to locate neural sources of the 40-Hz response in bilateral auditory cortex, left posterior cingulate cortex and left superior frontal gyrus. We found that added noise enhanced the 40-Hz response in all these areas. Moreover, added noise also increased the synchronization between these regions in alpha and gamma frequency bands both during and after the 40-Hz response. Our results demonstrate neural SR in several functionally specific brain regions, including areas not traditionally thought to contribute to the auditory 40-Hz transient response. In addition, we demonstrated SR in the synchronization between these brain regions. Thus, both intra- and inter-regional synchronization of neural activity are facilitated by the addition of moderate amounts of random noise. Because the noise levels in the brain fluctuate with arousal system activity, particularly across sleep-wake cycles, optimal neural noise levels, and thus SR, could be involved in optimizing the formation of task-relevant brain networks at several scales under normal conditions