Long-range temporal correlations in scale-free neuromorphic networks

© 2020 Massachusetts Institute of Technology. Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing

Technical note: successful DNA amplification of DNA from non-destructive buccal swabbing in Vespertilionid and Rhinolophid bats

\ua9 The Author(s) 2024.Acquiring DNA from wild bats (Mammalia: Chiroptera) is typically undertaken utilizing highly invasive (but non-lethal) sampling techniques comprising wing biopsies and occasional blood samples. While non-invasive sampling is possible through the extraction of DNA from faecal samples, it is not always possible to acquire samples from individual bats whilst conducting fieldwork, and as such, this method is primarily applicable to roost occupancy identification. Similarly, wing swabbing is liable to cross-contamination from roost mates. Here we present the first use of oral (buccal) swabbing for successful, species-resolution DNA sequencing of Vespertilionidae and Rhinolophidae in 10 bat species (nine Vespertilionidae and one Rhinolophidae) from the UK

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks.

Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network. The atomic-scale dynamics emulate leaky integrate and fire (LIF) mechanisms in biological neurons, leading to the generation of critical avalanches of signals. These avalanches are quantitatively the same as those observed in cortical tissue and are signatures of the correlations that are required for computation. We show that the avalanches are associated with dynamical restructuring of the networks which self-tune to balanced states consistent with self-organized criticality. Our simulations allow visualization of the network states and detailed mechanisms of signal propagation

Dynamic response of a thin sessile drop of conductive liquid to an abruptly applied or removed electric field

We consider, both theoretically and experimentally, a thin sessile drop of conductive liquid that rests on the lower plate of a parallel-plate capacitor. We derive analytical expressions for both the initial deformation and the relaxation dynamics of the drop as the electric field is either abruptly applied or abruptly removed, as functions of the geometrical, electrical, and material parameters, and investigate the ranges of validity of these expressions by comparison with full numerical simulations. These expressions provide a reasonable description of the experimentally measured dynamic response of a drop of conductive ionic liquid 1-butyl-3-methyl imidazolium tetrafluoroborate

Assessing population impacts of toxicant-induced disruption of breeding behaviours using an individual-based model for the three-spined stickleback

 This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThe effects of toxicant exposure on individuals captured in standard environmental risk assessments (ERA) do not necessarily translate proportionally into effects at the population-level. Population models can incorporate population resilience, physiological susceptibility, and likelihood of exposure, and can therefore be employed to extrapolate from individual- to population-level effects in ERA. Here, we present the development of an individual-based model (IBM) for the three-spined stickleback (Gasterosteus aculeatus) and its application in assessing population-level effects of disrupted male breeding behaviour after exposure to the anti-androgenic pesticide, fenitrothion. The stickleback is abundant in marine, brackish, and freshwater systems throughout Europe and their complex breeding strategy makes wild populations potentially vulnerable to the effects of endocrine disrupting chemicals (EDCs). Modelled population dynamics matched those of a UK field population and the IBM is therefore considered to be representative of a natural population. Literature derived dose-response relationships of fenitrothion-induced disruption of male breeding behaviours were applied in the IBM to assess population-level impacts. The modelled population was exposed to fenitrothion under both continuous (worst-case) and intermittent (realistic) exposure patterns and population recovery was assessed. The results suggest that disruption of male breeding behaviours at the individual-level cause impacts on population abundance under both fenitrothion exposure regimes; however, density-dependent processes can compensate for some of these effects, particularly for an intermittent exposure scenario. Our findings further demonstrate the importance of understanding life-history traits, including reproductive strategies and behaviours, and their density-dependence, when assessing the potential population-level risks of EDCs.Syngenta LtdBiotechnology and Biological Sciences Research Council (BBSRC

Does addition of craving management tools in a stop smoking app improve quit rates among adult smokers? Results from BupaQuit pragmatic pilot randomised controlled trial

Introduction: Delivery of craving management tools (CMTs) via smartphone applications (apps) may improve smoking cessation rates, but research on such programmes remains limited, especially in real-world settings. This study evaluated the effectiveness of adding CMTs in a cessation app (BupaQuit). / Methods: The study was a two-arm pragmatic pilot parallel randomised controlled trial, comparing a fully-automated BupaQuit app with CMT with a control app version without CMT. A total of 425 adult UK-based daily smokers were enrolled through open online recruitment (February 2015-March 2016), with no researcher involvement, and individually randomised within the app to the intervention (n=208) or control (n=217). The primary outcome was self-reported 14-day continuous abstinence assessed at 4-week follow-up. Secondary outcomes included 6-month point-prevalence and sustained abstinence, and app usage. The primary outcome was assessed with Fisher’s exact test using intent to treat with those lost to follow-up counted as smoking. Participants were not reimbursed. / Results: Re-contact rates were 50.4% at 4 weeks and 40.2% at 6 months. There was no significant difference between intervention and control arms on the primary outcome (13.5% vs 15.7%; p=0.58;RR=0.86, 95% Confidence Interval (CI)=0.54-1.36) or secondary cessation outcomes (6-month point prevalence: 14.4% vs. 17.1%, p=0.51;RR=0.85, 95%CI=0.54-1.32; 6-month sustained: 11.1% vs 13.4%, p=0.55,RR=0.83,95%CI=0.50-1.38). Bayes factors supported the null hypothesis (B[0, 0,1.0986]=.20). Usage was similar across the conditions (mean/median logins: 9.6/4 vs. 10.5/5; time spent: 401.8/202s vs. 325.8/209s). / Conclusions: The addition of craving management tools did not affect cessation, and the limited engagement with the app may have contributed to this