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Correlations between the spiking of pairs of neurons are often used to study the brain’s representation of sensory or motor variables and neural circuit function and dysfunction. Previous statistical techniques have shown how time-averaged spike-spike correlations can be predicted by the time-averaged relationships between the individual neurons and the local field potential (LFP). However, spiking and LFP are both nonstationary, and spike-spike correlations have nonstationary structure that cannot be accounted for by time-averaged approaches. Here we develop parametric models that predict spike-spike correlations using a small number of LFP-based predictors, and we then apply these models to the problem of tracking changes in spike-spike correlations over time. Parametric models allow for flexibility in the choice of which LFP recording channels and frequency bands to use for prediction, and coefficients directly indicate which LFP features drive correlated spiking. Here we demonstrate our methods in simulation and test the models on experimental data from large-scale multi-electrode recordings in the mouse hippocampus and visual cortex. In single time windows, we find that our parametric models can be as accurate as previous nonparametric approaches, while also being flexible and interpretable. We then demonstrate how parametric models can be applied to describe nonstationary spike-spike correlations measured in sequential time windows. We find that although the patterns of both cortical and hippocampal spike-spike correlations vary over time, these changes are, at least partially, predicted by models that assume a fixed spike-field relationship. This approach may thus help to better understand how the dynamics of spike-spike correlations are related to functional brain states. Since spike-spike correlations are increasingly used as features for decoding external variables from neural activity, these models may also have the potential to improve the accuracy of adaptive decoders and brain machine interfaces. 

We develop double microwave shielding, which has recently enabled evaporative cooling to the first Bose-Einstein condensate of polar molecules [Bigagli , Nature , 289 (2024)]. Two microwave fields of different frequency and polarization are employed to effectively shield polar molecules from inelastic collisions and three-body recombination. Here, we describe in detail the theory of double microwave shielding. We demonstrate that double microwave shielding effectively suppresses two- and three-body losses. Simultaneously, dipolar interactions and the scattering length can be flexibly tuned, enabling comprehensive control over interactions in ultracold gases of polar molecules. We show that this approach works universally for a wide range of molecules. This opens the door to studying many-body physics with strongly interacting dipolar quantum matter. 

Ensembles of particles governed by quantum mechanical laws exhibit fascinating emergent behavior. Atomic quantum gases, liquid helium, and electrons in quantum materials all show distinct properties due to their composition and interactions. Quantum degenerate samples of bosonic dipolar molecules promise the realization of novel phases of matter with tunable dipolar interactions and new avenues for quantum simulation and quantum computation. However, rapid losses, even when reduced through collisional shielding techniques, have so far prevented cooling to a Bose-Einstein condensate (BEC). In this work, we report on the realization of a BEC of dipolar molecules. By strongly suppressing two- and three-body losses via enhanced collisional shielding, we evaporatively cool sodium-cesium (NaCs) molecules to quantum degeneracy. The BEC reveals itself via a bimodal distribution and a phase-space-density exceeding one. BECs with a condensate fraction of 60(10) % and a temperature of 6(2) nK are created and found to be stable with a lifetime close to 2 seconds. This work opens the door to the exploration of dipolar quantum matter in regimes that have been inaccessible so far, promising the creation of exotic dipolar droplets, self-organized crystal phases, and dipolar spin liquids in optical lattices. 

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine: 1) decoding the direction of grating stimuli from spike recordings in primary visual cortex in monkeys, 2) decoding movement direction from recordings in primary motor cortex in monkeys, 3) decoding natural images from multi-region recordings in mice, and 4) decoding position from hippocampal recordings in rats. For each setting we characterize the overconfidence, and we describe a possible method to correct miscalibration post-hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain machine interfaces that more accurately reflect confidence levels when identifying external variables. Significance Statement Bayesian decoding is a statistical technique for making probabilistic predictions about external stimuli or movements based on recordings of neural activity. These predictions may be useful for robust brain machine interfaces or for understanding perceptual or behavioral confidence. However, the probabilities produced by these models do not always match the observed outcomes. Just as a weather forecast predicting a 50% chance of rain may not accurately correspond to an outcome of rain 50% of the time, Bayesian decoders of neural activity can be miscalibrated as well. Here we identify and measure miscalibration of Bayesian decoders for neural spiking activity in a range of experimental settings. We compare multiple statistical models and demonstrate how overconfidence can be corrected. 

We report on the design and characterization of a compact microwave antenna for atomic and molecular physics experiments. The antenna is comprised of four loop antennas arranged in a cloverleaf shape, allowing for precise adjustment of polarization by tuning the relative phase of the loops. We optimize the antenna for left-circularly polarized microwaves at 3.5 GHz and characterize its near-field performance using ultracold NaCs molecules as a precise quantum sensor. Observing an unusually high Rabi frequency of 2π × 46.1(2) MHz, we extract an electric field amplitude of 33(2) V/cm at 22 mm distance from the antenna. The polarization ellipticity is 2.3(4)°, corresponding to a 24 dB suppression of right-circular polarization. The cloverleaf antenna is planar and provides large optical access, making it highly suitable for quantum control of atoms and molecules and potentially other quantum systems that operate in the microwave regime. 

Abstract In many areas of the brain, neural spiking activity covaries with features of the external world, such as sensory stimuli or an animal's movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To flexibly track time-varying neural response properties, we developed a dynamic model with Conway-Maxwell Poisson (CMP) observations. The CMP distribution can flexibly describe firing patterns that are both under- and overdispersed relative to the Poisson distribution. Here we track parameters of the CMP distribution as they vary over time. Using simulations, we show that a normal approximation can accurately track dynamics in state vectors for both the centering and shape parameters (λ and ν). We then fit our model to neural data from neurons in primary visual cortex, “place cells” in the hippocampus, and a speed-tuned neuron in the anterior pretectal nucleus. We find that this method outperforms previous dynamic models based on the Poisson distribution. The dynamic CMP model provides a flexible framework for tracking time-varying non-Poisson count data and may also have applications beyond neuroscience. 

Theories of efficient coding propose that the auditory system is optimized for the statistical structure of natural sounds, yet the transformations underlying optimal acoustic representations are not well understood. Using a database of natural sounds including human speech and a physiologically-inspired auditory model, we explore the consequences of peripheral (cochlear) and mid-level (auditory midbrain) filter tuning transformations on the representation of natural sound spectra and modulation statistics. Whereas Fourier-based sound decompositions have constant time-frequency resolution at all frequencies, cochlear and auditory midbrain filters bandwidths increase proportional to the filter center frequency. This form of bandwidth scaling produces a systematic decrease in spectral resolution and increase in temporal resolution with increasing frequency. Here we demonstrate that cochlear bandwidth scaling produces a frequency-dependent gain that counteracts the tendency of natural sound power to decrease with frequency, resulting in a whitened output representation. Similarly, bandwidth scaling in mid-level auditory filters further enhances the representation of natural sounds by producing a whitened modulation power spectrum (MPS) with higher modulation entropy than both the cochlear outputs and the conventional Fourier MPS. These findings suggest that the tuning characteristics of the peripheral and mid-level auditory system together produce a whitened output representation in three dimensions (frequency, temporal and spectral modulation) that reduces redundancies and allows for a more efficient use of neural resources. This hierarchical multi-stage tuning strategy is thus likely optimized to extract available information and may underlies perceptual sensitivity to natural sounds.