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: G-protein coupled receptors (GPCRs) play a paramount role in diverse brain functions. Twenty years ago, GPCR activity was shown to be regulated by membrane potential in vitro, but whether the voltage dependence of GPCRs contributes to neuronal coding and behavioral output under physiological conditions in vivo has never been demonstrated. We show in two different processes that muscarinic GPCR mediated neuromodulation in vivo is voltage dependent. First, we show that muscarinic type A receptors (mAChR-A) mediated neuronal potentiation is voltage dependent. This potentiation voltage dependency is abolished in mutant flies expressing a voltage independent receptor. Most important, muscarinic receptor voltage independence caused a strong behavioral effect of increased odor habituation. Second, we show that muscarinic type B receptors (mAChR-B) voltage dependency is required for both efficient and accurate learning and memory. Normally, to prevent non-specific olfactory learning and memory, mAChR-B activity suppress both signals that are required for plasticity. Behavior experiments demonstrate that mAChR-B knockdown impairs olfactory learning by inducing undesired changes to the valence of an odor that was not associated with the reinforcer. On the other hand, mAChR-B voltage dependence prevents mAChR-B to interfere with plasticity in neurons that are required for the learning and memory process. Indeed, generating flies with a voltage independent mAChR-B resulted in impaired learning. Thus, we provide the very first demonstrations of physiological roles for the voltage dependency of GPCRs by demonstrating crucial involvement of GPCR voltage dependence in neuronal plasticity and behavior. As such, our findings create a paradigm shift in our thinking on GPCR recruitment and activity. Together, we suggest that GPCR voltage dependency plays a role in many diverse neuronal functions including learning and memory and may serve as a target for novel drug development.
Light refreshments before the seminar.

Large arrays of trapped neutral atoms have emerged over the past few years as a promising platform for quantum information processing, combining inherent scalability with high-fidelity control and site-resolved readout. In this talk, I will discuss ongoing work with arrays of Alkaline-earth atoms. These divalent atoms offer unique properties stemming largely from their long-lived metastable states, which form the basis of the optical atomic clock. I will describe the design of a universal quantum processor based on clock qubits and its application in quantum metrology, and I will address the challenge of generating and benchmarking highly entangled states in an analog quantum simulator. First, we realize scalable local control of individual clock qubits, which we utilize to extend the Ramsey interrogation time beyond the coherence time of a single atom [1]. To realize a universal quantum processor, we demonstrate record high-fidelity two-qubit entangling gates mediated by Rydberg interactions, which we combine with dynamical reconfiguration to entangle clock probes in GHZ states and perform Ancilla-based detection [2]. We then use the narrow clock transition to measure and remove thermal excitations of atoms in tweezers (a technique known as erasure conversion) and generate hyperentangled states of motion and spin [3]. In the second part of the talk, I will describe a different approach for generating large scale entangled states in an analog quantum simulator configuration [4], including error mitigation [5] and benchmarking of a 60-atom simulator [6]. Together, these show the great promise and the large variety of experiments accessed with this emerging platform. [1] A. Shaw*, R. Finkelstein*, R. Tsai, P. Scholl, T. Yoon, J. Choi, M. Endres, Multi-ensemble metrology by programming local rotations with atom movements, arxiv:2303.16885, Nature Physics in press (2023). [2] R. Finkelstein, R. Tsai, A. Shaw, X. Sun, M. Endres, A universal quantum processor for entanglement enhanced optical tweezer clocks, in preparation. [3] P. Scholl*, A. Shaw*, R. Finkelstein*, R. Tsai, J. Choi, M. Endres, Erasure cooling, control, and hyper-entanglement of motion in optical tweezers, arXiv:2311.15580 (2023). [4] J. Choi, A. Shaw, I. Madjarov, X. Xie, R. Finkelstein, J. Covey, J. Cotler, D. Mark, H.Y. Huang, A. Kale, H. Pichler, F. Brandão, S. Choi, and M. Endres, Preparing random states and benchmarking with many-body quantum chaos, Nature 617 (2023) [5] P. Scholl, A. Shaw, R. Tsai, R. Finkelstein, J. Choi, M. Endres, Erasure conversion in a high-fidelity Rydberg quantum simulator, Nature 622 (2023). [6] A. Shaw, Z. Chen, J. Choi, D.K. Mark, P. Scholl, R. Finkelstein, A. Elben, S. Choi, M. Endres, Benchmarking highly entangled states on a 60-atom analog quantum simulator, arXiv:2308.07914 (2023).

In living systems, the tissue micro-architecture consists of myriad cellular and subcellular elements whose density, size/shape distributions, composition, and permeability, endow the tissue with its biological functionality. Dynamic transport mechanisms are further critical for maintaining homeostasis and supporting diverse physiological functions such as action potentials and biochemical signaling. Still, how these biophysical properties change over time and how they couple to activity, remains largely unknown. This is mainly due to the difficulty in mapping these properties in-vivo, longitudinally, and with sufficient specificity. Magnetic Resonance Imaging (MRI), with its capacity for longitudinal studies and wealth of microscopic information leading to multiple contrast mechanisms, provides an outstanding opportunity to decipher these phenomena. In this talk I will discuss our recent advances in diffusion and functional MRI, including novel pulse sequences and biophysical modeling of diffusion processes in the microscopic tissue milieu, which provide, for the first time, the sought-after specificity for density, size, and permeability of particular (sub)cellular elements in tissues. I will show new experiments in rodents proving unique power-laws predicted from biophysical models, revealing axon density and size, as well as cell body density and size, along with validations against ground-truth histology and applications in animal models of disease. Evidence for exchange between the intracellular and extracellular space will also be given, along with a first approach for quantitatively mapping permeability in tissue. I will also introduce correlation tensor MRI (CTI), a new approach for source-separation in diffusional kurtosis, that offers surrogate markers of neurite beading effects, thereby further enhancing specificity, especially in stroke. Finally, I will touch upon dynamic modulations of neural tissue microstructure upon neural activity, and provide evidence for the existence of a neuro-morphological coupling in diffusion-weighted functional MRI signals. Future vistas and potential applications will be discussed.

To function as an integrated entity, the organism must synchronize between behavior and physiology. Our research focuses on probing this synchronization through the lens of the brain-immune system interface. The immune system, pivotal in preserving the organism's integrity, is also a sensitive barometer of its overall state. I will discuss the emerging understanding of how the brain represents the state of the immune system and the specific neural mechanisms that enable the brain to orchestrate immune responses.

I will present a theoretical framework for analyzing learning algorithms which rely on dependent, rather than independent, observations. While a common assumption is that the learning algorithm receives independent datapoints, such as unrelated images or texts, this assumption often does not hold. An example is data on opinions across a social network, where opinions of related people are often correlated, for example as a consequence of their interactions. I will present a line of work that models the dependence between such related datapoints using a probabilistic framework in which the observed datapoints are assumed to be sampled from some joint distribution, rather than sampled i.i.d. The joint distribution is modeled via the Ising model, which originated in the theory of Spin Glasses in statistical physics and was used in various research areas. We frame the problem of learning from dependent data as the problem of learning parameters of the Ising model, given a training set that consists of only a single sample from the joint distribution over all datapoints. We then propose using the Pseudo-MLE algorithm, and provide a corresponding analysis, improving upon the prior literature which necessitated multiple samples from this joint distribution. Our proof benefits from sparsifying a model's interaction network, conditioning on subsets of variables that make the dependencies in the resulting conditional distribution sufficiently weak. We use this sparsification technique to prove generic concentration and anti-concentration results for the Ising model, which have found applications beyond the scope of our work.

Based on joint work with Constantinos Daskalakis, Anthimos Vardis Kandiros, Nishanth Dikkala, Siddhartha Jayanti, Surbhi Goel and Davin Choo.