Central Executive Network drives delta-9-tetrahydrocannabinol (THC)-induced nonlinear changes in large-scale functional connectivity in adolescent nonhuman primates
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Latent circuit inference from heterogeneous neural responses during cognitive tasks
Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.
All-dielectric nonlinear metasurface: from visible to vacuum ultraviolet
All-dielectric nonlinear metasurfaces have higher damage thresholds and more flexible manipulation of resonant modes. Because of the intrinsic advantages of short-wavelength regions, related research gradually moved from the visible to the vacuum ultraviolet (VUV) region. This review introduces the theoretical framework: resonant modes enhance the local electromagnetic field and promote nonlinear polarization. After analyzing nonlinear efficiency enhancement mechanisms, we explore nonlinear modulation strategies. Finally, potential research directions are discussed.
Simultaneous tACS-fMRI reveals state- and frequency-specific modulation of hippocampal-cortical functional connectivity
Non-invasive indirect hippocampal-targeted stimulation is of broad scientific and clinical interest. Transcranial alternating current stimulation (tACS) is appealing because it allows oscillatory stimulation to study hippocampal theta (3–8 Hz) activity. We found that tACS administered during functional magnetic resonance imaging yielded a frequency-, mental state- and topologically-specific effect of theta stimulation (but not other frequencies) enhancing right (but not left) hippocampal-cortical connectivity during resting blocks but not during task blocks. Control analyses showed that this effect was not due to possible stimulation-induced changes in signal quality or head movement. Our findings are promising for targeted network modulations of deep brain structures for research and clinical intervention.
Universal relations and bounds for fluctuations in quasistatic small heat engines
The efficiency of any heat engine, defined as the ratio of average work output to heat input, is bounded by Carnot’s celebrated result. However, this measure is insufficient to characterize the properties of miniaturized heat engines carrying non-negligible fluctuations, and a study of higher-order statistics of their energy exchanges is required. Here, we generalize Carnot’s result for reversible cycles to arbitrary order moment of the work and heat fluctuations. Our results show that, in the quasistatic limit, higher-order statistics of a small engine’s energetics depend solely on the ratio between the temperatures of the thermal baths. We further prove that our result for the second moment gives universal bounds for the ratio between the variances of work and heat for quasistatic cycles. We test this theory with our previous experimental results of a Brownian Carnot engine and observe the consistency between them, even beyond the quasistatic regime. Our results can be exploited in the design of thermal nanomachines to reduce their fluctuations of work output without marginalizing its average value and efficiency.
Stabilization of Kerr-cat qubits with quantum circuit refrigerator
A periodically driven superconducting nonlinear resonator can implement a Kerr-cat qubit, which provides a promising route to a quantum computer with a long lifetime. However, the system is vulnerable to pure dephasing, which causes unwanted excitations outside the qubit subspace. Therefore, we require a refrigeration technology that confines the system in the qubit subspace. We theoretically study on-chip refrigeration for Kerr-cat qubits based on photon-assisted electron tunneling at tunneling junctions, called quantum circuit refrigerators (QCR). Rates of QCR-induced deexcitations of the system can be changed by more than four orders of magnitude by tuning a bias voltage across the tunneling junctions. Unwanted QCR-induced bit flips are greatly suppressed due to quantum interference in the tunneling process, and thus the long lifetime is preserved. The QCR can serve as a tunable dissipation source that stabilizes Kerr-cat qubits.
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