Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information

Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie.Wethen determined how the power of LFPs and spikes at different frequencies represents the visual features in the movie.Wefound that the most informative LFP frequency ranges were 1– 8 and 60 –100 Hz. LFPs in the range of 12– 40 Hz carried little information about the stimulus, and may primarily reflect neuromodulatory inputs. Spike power was informative only at frequencies <12 Hz. We further quantified “signal correlations” (correlations in the trial-averaged power response to different stimuli) and “noise correlations” (trial-by-trial correlations in the fluctuations around the average) of LFPs and spikes recorded from the same electrode. We found positive signal correlation between high-gamma LFPs (60 –100 Hz) and spikes, as well as strong positive signal correlation within high-gamma LFPs, suggesting that high-gamma LFPs and spikes are generated within the same network. LFPs<24 Hz shared strong positive noise correlations, indicating that they are influenced by a common source, such as a diffuse neuromodulatory input. LFPs<40 Hz showed very little signal and noise correlations with LFPs>40Hzand with spikes, suggesting that low-frequency LFPs reflect neural processes that in natural conditions are fully decoupled from those giving rise to spikes and to high-gamma LFPs

Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data

Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals recorded at multiple electrodes. We recently introduced the MERLiN (Mixture Effect Recovery in Linear Networks) algorithm that is able to recover, from an observed linear mixture, a causal variable that is a linear effect of another given variable. Here we relax the assumption of this cause-effect relationship being linear and present an extended algorithm that can pick up non-linear cause-effect relationships. Thus, the main contribution is an algorithm (and ready to use code) that has broader applicability and allows for a richer model class. Furthermore, a comparative analysis indicates that the assumption of linear cause-effect relationships is not restrictive in analysing electroencephalographic data

Successful network inference from time-series data using Mutual Information Rate

This work uses an information-based methodology to infer the connectivity of complex systems from observed time-series data. We first derive analytically an expression for the Mutual Information Rate (MIR), namely, the amount of information exchanged per unit of time, that can be used to estimate the MIR between two finite-length low-resolution noisy time-series, and then apply it after a proper normalization for the identification of the connectivity structure of small networks of interacting dynamical systems. In particular, we show that our methodology successfully infers the connectivity for heterogeneous networks, different time-series lengths or coupling strengths, and even in the presence of additive noise. Finally, we show that our methodology based on MIR successfully infers the connectivity of networks composed of nodes with different time-scale dynamics, where inference based on Mutual Information fails

Detecting Generalized Synchronization Between Chaotic Signals: A Kernel-based Approach

A unified framework for analyzing generalized synchronization in coupled chaotic systems from data is proposed. The key of the proposed approach is the use of the kernel methods recently developed in the field of machine learning. Several successful applications are presented, which show the capability of the kernel-based approach for detecting generalized synchronization. It is also shown that the dynamical change of the coupling coefficient between two chaotic systems can be captured by the proposed approach.Comment: 20 pages, 15 figures. massively revised as a full paper; issues on the choice of parameters by cross validation, tests by surrogated data, etc. are added as well as additional examples and figure

The European Photon Imaging Camera on XMM-Newton: The MOS Cameras

The EPIC focal plane imaging spectrometers on XMM-Newton use CCDs to record the images and spectra of celestial X-ray sources focused by the three X-ray mirrors. There is one camera at the focus of each mirror; two of the cameras contain seven MOS CCDs, while the third uses twelve PN CCDs, defining a circular field of view of 30 arcmin diameter in each case. The CCDs were specially developed for EPIC, and combine high quality imaging with spectral resolution close to the Fano limit. A filter wheel carrying three kinds of X-ray transparent light blocking filter, a fully closed, and a fully open position, is fitted to each EPIC instrument. The CCDs are cooled passively and are under full closed loop thermal control. A radio-active source is fitted for internal calibration. Data are processed on-board to save telemetry by removing cosmic ray tracks, and generating X-ray event files; a variety of different instrument modes are available to increase the dynamic range of the instrument and to enable fast timing. The instruments were calibrated using laboratory X-ray beams, and synchrotron generated monochromatic X-ray beams before launch; in-orbit calibration makes use of a variety of celestial X-ray targets. The current calibration is better than 10% over the entire energy range of 0.2 to 10 keV. All three instruments survived launch and are performing nominally in orbit. In particular full field-of-view coverage is available, all electronic modes work, and the energy resolution is close to pre-launch values. Radiation damage is well within pre-launch predictions and does not yet impact on the energy resolution. The scientific results from EPIC amply fulfil pre-launch expectations.Comment: 9 pages, 11 figures, accepted for publication in the A&A Special Issue on XMM-Newto

Decision-Theoretic Planning with non-Markovian Rewards

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPPs treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter

Action oriented ESD for community benefit: two sustainability audit case studies

* Please enter authors as Gretton, S. and Reeves, A.Education for Sustainable Development (ESD) practice can be characterised by 'action-oriented' pedagogies, which aim to deliver mutual benefit for learners and community stakeholders. Such approaches, enable ESD to also address other higher education priorities such as employability, enhanced student experience and the civic university agenda. This contribution offers two contrasting case studies of an action-oriented approach, whereby students are partnered with local businesses to evaluate their sustainability impacts. At University of Leicester (UoL), a Sustainability Audit process originally delivered by staff has been adapted into a credit-bearing 'work-related learning module', delivered with undergraduate science students from eight programmes. Working with real-world data and interdisciplinary approaches, students produce an evidence-based recommendations report for businesses, developing professional competencies as 'change-makers'. The UoL audit process was shared with De Montfort University (DMU) in a joint project where students were trained and paid to deliver sustainability audits. The process was revised into a user-friendly self-completion spreadsheet, designed for use without prior training. This entry-level process enables large-scale reach, potentially within hundreds of employer placements taking place through DMU annually, achieving real-world impact. Taken together, the case studies demonstrate cross-fertilisation between local universities and formal/informal curriculum linkages, highlighting diverse strategies for pursuing the ESD agenda

Scheme Planning, Artificial Intelligence and Student Teachers: A Cautionary Tale

The authors delve into the critical role of lesson planning in developing effective teachers, highlighting how it integrates pedagogical, curricular, and subject knowledge. They discuss the shift from teachers creating personalized lesson plans to relying on predesigned schemes—a trend accelerated by the introduction of the Mastery curriculum in 2014 and the COVID-19 pandemic. While acknowledging efforts to reduce teacher workloads, Gretton and Lea caution that over-reliance on ready-made plans and artificial intelligence tools may hinder student teachers' ability to understand and meet their learners' diverse needs