A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Modelling the Influence of Foot-and-Mouth Disease Vaccine Antigen Stability and Dose on the Bovine Immune Response

Foot and mouth disease virus causes a livestock disease of significant global socio-economic importance. Advances in its control and eradication depend critically on improvements in vaccine efficacy, which can be best achieved by better understanding the complex within-host immunodynamic response to inoculation. We present a detailed and empirically parametrised dynamical mathematical model of the hypothesised immune response in cattle, and explore its behaviour with reference to a variety of experimental observations relating to foot and mouth immunology. The model system is able to qualitatively account for the observed responses during in-vivo experiments, and we use it to gain insight into the incompletely understood effect of single and repeat inoculations of differing dosage using vaccine formulations of different structural stability

Erosional truncation of uppermost Permian shallow-marine carbonates and implications for Permian-Triassic boundary events

On shallow-marine carbonate buildups in south China, Turkey, and Japan, uppermost Permian skeletal limestones are truncated by an erosional surface that exhibits as much as 10 cm of topography, including overhanging relief. Sedimentary facies, microfabrics, carbon isotopes, and cements together suggest that erosion occurred in a submarine setting. Moreover, biostratigraphic data from south China demonstrate that the surface postdates the uppermost Permian sequence boundary at the global stratotype section and truncates strata within the youngest known Permian conodont zone. The occurrences of similar truncation surfaces at the mass-extinction horizon on carbonate platforms across the global tropics, each overlain by microbial buildups, and their association with a large negative excursion in delta C-13 further suggest a causal link between erosion of shallow-marine carbonates and mass extinction. Previously proposed to account for marine extinctions, the hypothesis of rapid carbon release from sedimentary reservoirs or the deep ocean can also explain the petro-graphic observations. Rapid, unbuffered carbon release would cause submarine carbonate dissolution, accounting for erosion of uppermost Permian skeletal carbonates, and would be followed by a pulse of high carbonate saturation, explaining the precipitation of microbial limestones containing upward-growing carbonate crystal fans. Models for other carbon-release events suggest that at least 5 x 1018 g of carbon, released in < 100 key., would be required. Of previously hypothesized Permian-Triassic boundary scenarios, thermogenic methane production from heating of coals during Siberian Traps emplacement best accounts for petrographic characteristics and depositional environment of the truncation surface and overlying microbial limestone, as well as an associated carbon isotope excursion and physiologically selective extinction in the marine realm

Data from: Elevated CO2 and water addition enhance nitrogen turnover in grassland plants with implications for temporal stability

Temporal variation in soil nitrogen (N) availability affects growth of grassland communities that differ in their use and reuse of N. In a seven-year-long climate change experiment in a semiarid grassland, the temporal stability of plant biomass production varied with plant N turnover (reliance on externally acquired N relative to internally recycled N). Species with high N turnover were less stable in time compared to species with low N turnover. In contrast, N turnover at the community level was positively associated with asynchrony in biomass production, which in turn increased community temporal stability. Elevated CO2 and summer irrigation, but not warming, enhanced community N turnover and stability, possibly because treatments promoted greater abundance of species with high N turnover. Our study highlights the importance of plant N turnover for determining the temporal stability of individual species and plant communities affected by climate change

Data from: Elevated CO2 and water addition enhance nitrogen turnover in grassland plants with implications for temporal stability

Temporal variation in soil nitrogen (N) availability affects growth of grassland communities that differ in their use and reuse of N. In a seven-year-long climate change experiment in a semiarid grassland, the temporal stability of plant biomass production varied with plant N turnover (reliance on externally acquired N relative to internally recycled N). Species with high N turnover were less stable in time compared to species with low N turnover. In contrast, N turnover at the community level was positively associated with asynchrony in biomass production, which in turn increased community temporal stability. Elevated CO2 and summer irrigation, but not warming, enhanced community N turnover and stability, possibly because treatments promoted greater abundance of species with high N turnover. Our study highlights the importance of plant N turnover for determining the temporal stability of individual species and plant communities affected by climate change

Elevated CO2 and water addition enhance nitrogen turnover in grassland plants with implications for temporal stability

Temporal variation in soil nitrogen (N) availability affects growth of grassland communities that differ in their use and reuse of N. In a 7‐year‐long climate change experiment in a semi‐arid grassland, the temporal stability of plant biomass production varied with plant N turnover (reliance on externally acquired N relative to internally recycled N). Species with high N turnover were less stable in time compared to species with low N turnover. In contrast, N turnover at the community level was positively associated with asynchrony in biomass production, which in turn increased community temporal stability. Elevated CO2 and summer irrigation, but not warming, enhanced community N turnover and stability, possibly because treatments promoted greater abundance of species with high N turnover. Our study highlights the importance of plant N turnover for determining the temporal stability of individual species and plant communities affected by climate change