Neuroenhancement in Military Personnel::Conceptual and Methodological Promises and Challenges

Military personnel face harsh conditions that strain their physical and mental well-being, depleting resources necessary for sustained operational performance. Future operations will impose even greater demands on soldiers in austere environments with limited support, and new training and technological approaches are essential. This report highlights the progress in cognitive neuroenhancement research, exploring techniques such as neuromodulation and neurofeedback, and emphasizes the inherent challenges and future directions in the field of cognitive neuroenhancement for selection, training, operations, and recovery

Neural Reconstruction Integrity: A Metric for Assessing the Connectivity Accuracy of Reconstructed Neural Networks

Neuroscientists are actively pursuing high-precision maps, or graphs consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric for measuring the performance of such methods by comparing their output with those generated by human annotators (“ground truth” data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the “integrity” of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors. Demonstration code is available

Neural Reconstruction Integrity: A Metric for Assessing the Connectivity Accuracy of Reconstructed Neural Networks

Neuroscientists are actively pursuing high-precision maps, or graphs consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric for measuring the performance of such methods by comparing their output with those generated by human annotators (“ground truth” data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the “integrity” of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors. Demonstration code is available

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints increased network robustness on the most challenging examples in the CIFAR-10-C computer vision robustness benchmark task, while reducing learnable attention parameters by over an order of magnitude. Taken together, these results demonstrate multiple potential approaches to utilize insight from neural systems for developing robust and efficient machine learning techniques.Comment: 11 pages, 4 figure

Search for Neutral Higgs Bosons in Events with Multiple Bottom Quarks at the Tevatron

The combination of searches performed by the CDF and D0 collaborations at the Fermilab Tevatron Collider for neutral Higgs bosons produced in association with b quarks is reported. The data, corresponding to 2.6 fb-1 of integrated luminosity at CDF and 5.2 fb-1 at D0, have been collected in final states containing three or more b jets. Upper limits are set on the cross section multiplied by the branching ratio varying between 44 pb and 0.7 pb in the Higgs boson mass range 90 to 300 GeV, assuming production of a narrow scalar boson. Significant enhancements to the production of Higgs bosons can be found in theories beyond the standard model, for example in supersymmetry. The results are interpreted as upper limits in the parameter space of the minimal supersymmetric standard model in a benchmark scenario favoring this decay mode.Comment: 10 pages, 2 figure

Search for the standard model Higgs boson decaying to a bb pair in events with one charged lepton and large missing transverse energy using the full CDF data set

We present a search for the standard model Higgs boson produced in association with a W boson in sqrt(s) = 1.96 TeV p-pbar collision data collected with the CDF II detector at the Tevatron corresponding to an integrated luminosity of 9.45 fb-1. In events consistent with the decay of the Higgs boson to a bottom-quark pair and the W boson to an electron or muon and a neutrino, we set 95% credibility level upper limits on the WH production cross section times the H->bb branching ratio as a function of Higgs boson mass. At a Higgs boson mass of 125 GeV/c2 we observe (expect) a limit of 4.9 (2.8) times the standard model value.Comment: Submitted to Phys. Rev. Lett (v2 contains clarifications suggested by PRL

Search for the standard model Higgs boson decaying to a bbˉb\bar{b} pair in events with no charged leptons and large missing transverse energy using the full CDF data set

We report on a search for the standard model Higgs boson produced in association with a vector boson in the full data set of proton-antiproton collisions at $\sqrt{s} = 1.96$ TeV recorded by the CDF II detector at the Tevatron, corresponding to an integrated luminosity of 9.45 fb$^{-1}$. We consider events having no identified charged lepton, a transverse energy imbalance, and two or three jets, of which at least one is consistent with originating from the decay of a $b$ quark. We place 95% credibility level upper limits on the production cross section times standard model branching fraction for several mass hypotheses between 90 and $150 \mathrm{GeV}/c^2$. For a Higgs boson mass of $125 \mathrm{GeV}/c^2$, the observed (expected) limit is 6.7 (3.6) times the standard model prediction.Comment: Accepted by Phys. Rev. Let

Search for the standard model Higgs boson decaying to a bb pair in events with two oppositely-charged leptons using the full CDF data set

We present a search for the standard model Higgs boson produced in association with a Z boson in data collected with the CDF II detector at the Tevatron, corresponding to an integrated luminosity of 9.45/fb. In events consistent with the decay of the Higgs boson to a bottom-quark pair and the Z boson to electron or muon pairs, we set 95% credibility level upper limits on the ZH production cross section times the H -> bb branching ratio as a function of Higgs boson mass. At a Higgs boson mass of 125 GeV/c^2 we observe (expect) a limit of 7.1 (3.9) times the standard model value.Comment: To be submitted to Phys. Rev. Let