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In many circuit models of neural computation, synaptic connections between neurons are organized according to their tuning to the variables being processed. The connectivities of canonical neural network models of head direction, spatial navigation, and orientation selectivity obey this principle and contain symmetries related to the angular and spatial variables they operate on. We develop a graph embedding algorithm to identify such symmetries. The algorithm segregates structure related to cell type from that related to the symmetries we seek to identify, distinguishing it from standard embedding methods. Our method successfully identifies rotational and translational symmetries in heading direction and visual projection neuron circuits using a connectome of the adultDrosophilabrain, and it also identifies a toroidal symmetry in a synthetic connectome of grid cells in the medial entorhinal cortex. Such embedding geometries reveal the latent variables that are processed by a neural circuit and which cell types are responsible for this computation. 

Recent developments in quantum technologies have enabled significant improvements in the precision of optical sensing systems. This work explores the integration of distributed quantum sensing with optical gyroscopes to improve the estimation accuracy of angular velocity. Utilizing bright two-mode squeezed states (bTMSS), which offer high photon numbers and strong bipartite quantum correlations, we propose a configuration that leverages continuous-variable entanglement across multiple spatially separated optical gyroscopes. Unlike traditional quantum sensing that enhances a single sensor, our approach focuses on estimating a global phase shift—corresponding to the average angular rotation—across distributed optical gyroscopes with quantum-enhanced sensitivity. We analyze the phase sensitivities of different bTMSS configurations, including mode entangled bTMSS and separable $M$ -bTMSS, and evaluate their performance through the quantum Cramér-Rao bound. The analysis shows that, with $5\%$ photon loss in every channel in the system, the proposed scheme shows a sensitivity enhancement of $\sim 9.3\mathrm{dB}$ beyond the shot-noise limit, with an initial squeezing of $\sim 9.8\mathrm{dB}$ . The present scheme has potential applications in quantum-enhanced inertial navigation and precision metrology within emerging quantum networks. 

Abstract Carbon Nanofibers and fiber mats with thermal management properties are gaining attention for applications like energy storage, thermal insulation, and comfort. Optimal performance requires these materials to be lightweight, cost-effective, and have high thermal conductivity. Nanodiamonds, with their exceptional mechanical, electrical, thermal, and optical qualities, are promising enhancers for thermal management in electronics. They improve device performance significantly when applied to carbon nanofibers and mats. However, traditional nanodiamond deposition methods, such as spin and dip coating, often face challenges through poor uniformity, material waste, and fiber damage. To address this, we utilized an electrostatic corona discharge method, an innovative and efficient bottom-up approach that avoids contamination and enhances the thermal and mechanical properties of fibers through the elimination of polymer binders. This method creates an ionized field to attach nanodiamond particles evenly to fiber surfaces. Characterization techniques, including VHX microscope, Raman spectroscopy, and thermal conductivity tests, confirmed significant improvements in carbon nanofiber mats performance where the thermal conductivity was increased by approximately 85.7% upon coating with nanodiamonds. These enhanced materials surpass conventional polymer-based solutions, offering improved thermal conductivity due to optimized nanodiamond size, voltage application, and uniformity. They show promise as multifunctional nanocomposites for applications like flexible electronics, thermal management, and intelligent wearables. 

Control over the correlations between spatial modes in quantum states of light allows high-capacity secure information encoding. 

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective ‘teacher’ by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the ‘student’ compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists. 

The Drosophila mushroom body exhibits dopamine dependent synaptic plasticity that underlies the acquisition of associative memories. Recordings of dopamine neurons in this system have identified signals related to external reinforcement such as reward and punishment. However, other factors including locomotion, novelty, reward expectation, and internal state have also recently been shown to modulate dopamine neurons. This heterogeneity is at odds with typical modeling approaches in which these neurons are assumed to encode a global, scalar error signal. How is dopamine dependent plasticity coordinated in the presence of such heterogeneity? We develop a modeling approach that infers a pattern of dopamine activity sufficient to solve defined behavioral tasks, given architectural constraints informed by knowledge of mushroom body circuitry. Model dopamine neurons exhibit diverse tuning to task parameters while nonetheless producing coherent learned behaviors. Notably, reward prediction error emerges as a mode of population activity distributed across these neurons. Our results provide a mechanistic framework that accounts for the heterogeneity of dopamine activity during learning and behavior.