Extracting causal connections can advance interpretable AI and machine learning. Granger causality (GC) is a robust statistical method for estimating directed influences (DC) between signals. While GC has been widely applied to analysing neuronal signals in biological neural networks and other domains, its application to complex, nonlinear, and multistable neural networks is less explored. In this study, we applied time-domain multi-variate Granger causality (MVGC) to the time series neural activity of all nodes in a trained multistable biologically based decision neural network model with real-time decision uncertainty monitoring. Our analysis demonstrated that challenging two-choice decisions, where input signals could be closely matched, and the appropriate application of fine-grained sliding time windows, could readily reveal the original model's DC. Furthermore, the identified DC varied based on whether the network had correct or error decisions. Integrating the identified DC from different decision outcomes recovered most of the original model's architecture, despite some spurious and missing connectivity. This approach could be used as an initial exploration to enhance the interpretability and transparency of dynamic multistable and nonlinear biological or AI systems by revealing causal connections throughout different phases of neural network dynamics and outcomes.

Simultaneous localisation and mapping (SLAM) algorithms are commonly used in robotic systems for learning maps of novel environments. Brains also appear to learn maps, but the mechanisms are not known and it is unclear how to infer these maps from neural activity data. We present BrainSLAM; a method for performing SLAM using only population activity (local field potential, LFP) data simultaneously recorded from three brain regions in rats: hippocampus, prefrontal cortex, and parietal cortex. This system uses a convolutional neural network (CNN) to decode velocity and familiarity information from wavelet scalograms of neural local field potential data recorded from rats as they navigate a 2D maze. The CNN's output drives a RatSLAM-inspired architecture, powering an attractor network which performs path integration plus a separate system which performs `loop closure' (detecting previously visited locations and correcting map aliasing errors). Together, these three components can construct faithful representations of the environment while simultaneously tracking the animal's location. This is the first demonstration of inference of a spatial map from brain recordings. Our findings expand SLAM to a new modality, enabling a new method of mapping environments and facilitating a better understanding of the role of cognitive maps in navigation and decision making.

Multimodal integration is an important process in perceptual decision-making. In humans, this process has often been shown to be statistically optimal, or near optimal: sensory information is combined in a fashion that minimises the average error in perceptual representation of stimuli. However, sometimes there are costs that come with the optimization, manifesting as illusory percepts. We review audio-visual facilitations and illusions that are products of multisensory integration, and the computational models that account for these phenomena. In particular, the same optimal computational model can lead to illusory percepts, and we suggest that more studies should be needed to detect and mitigate these illusions, as artefacts in artificial cognitive systems. We provide cautionary considerations when designing artificial cognitive systems with the view of avoiding such artefacts. Finally, we suggest avenues of research towards solutions to potential pitfalls in system design. We conclude that detailed understanding of multisensory integration and the mechanisms behind audio-visual illusions can benefit the design of artificial cognitive systems.

The culture secretary Jeremy Wright is to question Tinder and Grindr about measures used to protect children after police records showed they are at risk of grooming and sexual exploitation on the dating apps. The Secretary of State for Digital, Culture, Media and Sport (DCMS) said he was "truly shocked" to discover the perpetrators of child sex offences had used online dating services. Mr Wright said: "I will be writing to these companies asking what measures they have in place to keep children safe from harm, including verifying their age. "If I'm not satisfied with their response, I reserve the right to take further action." Police have investigated more than 30 incidents of child rape since 2015 where victims were sexually exploited after evading age checks on dating apps, according to The Sunday Times. Dwain Chambers made his sprint comeback in the 60m event at the British Indoor Championships. The 40-year-old came in second during his heat with a time of 6.78 however after a ...

LONDONDERRY, Northern Ireland (Reuters) - There was a large bang and black smoke emerged from an abandoned van in a cordoned off area of Londonderry on Monday after an army bomb disposal robot entered the vehicle, a Reuters witness said on Monday.

Steve Worswick, botmaster of Mitsuku, was awarded the bronze medal and $4000 cash prize for creating the world's "most human computer" in the Loebner Prize Contest 2013, an annual Turing Test. The contest this year was held at the Ulster University, Magee Campus, Londonderry/Derry, Northern Ireland. Steve Worswick is a native of Yorkshire, UK, and has worked on Mitsuku for 9 years. This year the Loebner Prize Contest attracted 15 entries from around the world. Pandorabots submitted 6 of those entries, based on the results of an internal Divabot contest to select the best, most unique AIML bots hosted by Pandorabots.