230 research outputs found
Agents in proactive environments
Agents situated in proactive environments are acting au-tonomously while the environment is evolving alongside, whether or not the agents carry out any particular actions. A formal framework for simulating and reasoning about this generalized kind of dynamic systems is proposed. The capabilities of the agents are modeled by a set of conditional rules in a temporal-logical format. The environment itself is modeled by an independent transition relation on the state space. The temporal language is given a declarative semantics
PHP11 ESTIMATING THE ABILITY-TO-PAY FOR HEALTH CARE EXPENDITURES RISING FASTER THAN GDP: AN INTERNATIONAL PERSPECTIVE COMPARING THE USA AND GERMANY
Feasibility and resolution limits of opto-magnetic imaging of neural network activity in brain slices using color centers in diamond
We suggest a novel approach for wide-field imaging of the neural network
dynamics of brain slices that uses highly sensitivity magnetometry based
on nitrogen-vacancy (NV) centers in diamond. Invitro recordings in brain
slices is a proven method for the characterization of electrical neural
activity and has strongly contributed to our understanding of the
mechanisms that govern neural information processing. However, this
traditional approach only acquires signals from a few positions, which
severely limits its ability to characterize the dynamics of the
underlying neural networks. We suggest to extend its scope using NV
magnetometry-based imaging of the neural magnetic fields across the
slice. Employing comprehensive computational simulations and theoretical
analyses, we determine the spatiotemporal characteristics of the neural
fields and the required key performance parameters of an NV
magnetometry-based imaging setup. We investigate how the technical
parameters determine the achievable spatial resolution for an optimal 2D
reconstruction of neural currents from the measured field distributions.
Finally, we compare the imaging of neural slice activity with that of a
single planar pyramidal cell. Our results suggest that imaging of slice
activity will be possible with the upcoming generation of NV magnetic
field sensors, while single-shot imaging of planar cell activity remains
challenging
Commentary:Transcranial stimulation of the frontal lobes increases propensity of mind-wandering without changing meta-awareness
Commentary:Transcranial stimulation of the frontal lobes increases propensity of mind-wandering without changing meta-awareness
Blinding is compromised for transcranial direct current stimulation at 1Â mA for 20Â min in young healthy adults
Commentary:Transcranial stimulation of the frontal lobes increases propensity of mind-wandering without changing meta-awareness
A Commentary on
Transcranial stimulation of the frontal lobes increases propensity of mind-wandering without changing meta-awareness
by Axelrod, V., Zhu, X., & Qui, J. (2018). Scientific Reports, 8:15975. doi: 10.1038/s41598-018-34098-
A common framework for learning causality
[EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to understand the behaviour of an agent or to identify the relationship between two entities. Causality occurs when an action is taken and may also occur when two happenings come undeniably together. The study of causal inference aims at uncovering causal dependencies among observed data and to come up with automated methods to find such dependencies. While there exist a broad range of principles and approaches involved in causal inference, in this position paper we argue that it is possible to unify different causality views under a common framework of symbolic learning.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government.Onaindia De La Rivaherrera, E.; Aineto, D.; JimĂŠnez-Celorrio, S. (2018). A common framework for learning causality. Progress in Artificial Intelligence. 7(4):351-357. https://doi.org/10.1007/s13748-018-0151-yS35135774Aineto, D., JimĂŠnez, S., Onaindia, E.: Learning STRIPS action models with classical planning. In: International Conference on Automated Planning and Scheduling, ICAPS-18 (2018)Amir, E., Chang, A.: Learning partially observable deterministic action models. J. Artif. Intell. Res. 33, 349â402 (2008)Asai, M., Fukunaga, A.: Classical planning in deep latent space: bridging the subsymbolicâsymbolic boundary. In: National Conference on Artificial Intelligence, AAAI-18 (2018)Cresswell, S.N., McCluskey, T.L., West, M.M.: Acquiring planning domain models using LOCM. Knowl. Eng. Rev. 28(02), 195â213 (2013)Ebert-Uphoff, I.: Two applications of causal discovery in climate science. In: Workshop Case Studies of Causal Discovery with Model Search (2013)Ebert-Uphoff, I., Deng, Y.: Causal discovery from spatio-temporal data with applications to climate science. In: 13th International Conference on Machine Learning and Applications, ICMLA 2014, Detroit, MI, USA, 3â6 December 2014, pp. 606â613 (2014)Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H.: Nonmonotonic causal theories. Artif. Intell. 153(1â2), 49â104 (2004)Halpern, J.Y., Pearl, J.: Causes and explanations: a structural-model approach. Part I: Causes. Br. J. Philos. Sci. 56(4), 843â887 (2005)Heckerman, D., Meek, C., Cooper, G.: A Bayesian approach to causal discovery. In: Jain, L.C., Holmes, D.E. (eds.) Innovations in Machine Learning. Theory and Applications, Studies in Fuzziness and Soft Computing, chapter 1, pp. 1â28. Springer, Berlin (2006)Li, J., Le, T.D., Liu, L., Liu, J., Jin, Z., Sun, B.-Y., Ma, S.: From observational studies to causal rule mining. ACM TIST 7(2), 14:1â14:27 (2016)Malinsky, D., Danks, D.: Causal discovery algorithms: a practical guide. Philos. Compass 13, e12470 (2018)McCain, N., Turner, H.: Causal theories of action and change. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference, AAAI 97, IAAI 97, 27â31 July 1997, Providence, Rhode Island, pp. 460â465 (1997)McCarthy, J.: Epistemological problems of artificial intelligence. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, USA, 22â25 August 1977, pp. 1038â1044 (1977)McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. Mach. Intell. 4, 463â502 (1969)Pearl, J.: Reasoning with cause and effect. AI Mag. 23(1), 95â112 (2002)Pearl, J.: Causality: Models, Reasoning and Inference, 2nd edn. Cambridge University Press, Cambridge (2009)Spirtes, C.G.P., Scheines, R.: Causation, Prediction and Search, 2nd edn. The MIT Press, Cambridge (2001)Spirtes, P., Zhang, K.: Causal discovery and inference: concepts and recent methodological advances. Appl. Inform. 3, 3 (2016)Thielscher, M.: Ramification and causality. Artif. Intell. 89(1â2), 317â364 (1997)Triantafillou, S., Tsamardinos, I.: Constraint-based causal discovery from multiple interventions over overlapping variable sets. J. Mach. Learn. Res. 16, 2147â2205 (2015)Yang, Q., Kangheng, W., Jiang, Y.: Learning action models from plan examples using weighted MAX-SAT. Artif. Intell. 171(2â3), 107â143 (2007)Zhuo, H.H., Kambhampati, S: Action-model acquisition from noisy plan traces. In: International Joint Conference on Artificial Intelligence, IJCAI-13, pp. 2444â2450. AAAI Press (2013
Spatiotemporal structure of intracranial electric fields induced by transcranial electric stimulation in humans and nonhuman primates
Transcranial electric stimulation (TES) is an emerging technique, developed to non-invasively modulate brain function. However, the spatiotemporal distribution of the intracranial electric fields induced by TES remains poorly understood. In particular, it is unclear how much current actually reaches the brain, and how it distributes across the brain. Lack of this basic information precludes a firm mechanistic understanding of TES effects. In this study we directly measure the spatial and temporal characteristics of the electric field generated by TES using stereotactic EEG (s-EEG) electrode arrays implanted in cebus monkeys and surgical epilepsy patients. We found a small frequency dependent decrease (10%) in magnitudes of TES induced potentials and negligible phase shifts over space. Electric field strengths were strongest in superficial brain regions with maximum values of about 0.5 mV/mm. Our results provide crucial information of the underlying biophysics in TES applications in humans and the optimization and design of TES stimulation protocols. In addition, our findings have broad implications concerning electric field propagation in non-invasive recording techniques such as EEG/MEG
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