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Hippocampal place cells fire at a high rate whenever an animal is in a specific location in an environment and are thought to support spatial and episodic memory. When an animal visits different environments, place cells typically ‘remap’ (i.e., change their preferred locations), and when revisiting the same environment, the same spatial code reemerges. In a recent study by our lab, place cells were shown to globally remap, forming multiple distinct representations (maps) of the same environment that stably coexist across time. In that study, switching between different maps of the same environment happened only after the mice were disconnected from the environment.
            Here I performed a set of experiments to further understand the mechanism underlying switching between multiple maps. My project established a way to manipulate this mechanism, both through external orientation inputs and by acting directly on the hippocampal network state using optogenetics. My results provide support for the proposed role of head-direction or other orientation signals in the switching between maps. They also support the model of maps as stable attractors, where the specific attractor (map) used depends on the initial conditions of the network.

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Many problems of interest, ranging from condensed matter physics and quantum chemistry to quantum information, require finding the ground state of a system of many interacting degrees of freedom (e.g., qubits or quantum spins). The main challenge stems from the exponential scaling of the Hilbert space dimension with the number of qubits. I will first discuss various strategies to tackle this problem using classical computers, such as tensor network states and Monte Carlo sampling, and their limitations. Quantum computers are ideally suited for this task; I will present a proposal to simulate quantum systems on noisy intermediate-scale quantum (NISQ) devices made of imperfect qubits, where the noise level translates into a finite energy density (i.e., finite temperature).

Making decisions, attending to certain items, and manipulating information in working memory are fundamental behaviors that rely on specific neural circuitry. Throughout my research I have contributed to understanding such behaviors in human and in nonhuman primates but found that despite tremendous advances in the field, we still lack a mechanistic understanding of what goes wrong in conditions such as dementia or autism. My long-term research goal is to determine the circuits that support cognitive behavior, in health and disease.
In my talk, I present three key contributions I have made towards uncovering neuronal circuits for cognition in the macaque, an animal model whose neural circuitry affords unique insight into human brain function. First, I demonstrate the utility of rigorous psychophysical frameworks in determining the causal contribution of key brain regions to behavior in a perceptual decision-making task. Next, I describe how causal manipulations of brain areas involved in attentional control can be used to identify hitherto unknown areas and reveal new functional circuits in support of selective attention and object recognition. Finally, I show how computational analyses of population data reveal circuits within circuits with distinct roles in supporting working memory.
I end the talk by presenting my future research directions and approach: to leverage my experience studying how we select from external information (from sensory signals) to investigate how we select from internal information (from information stored in visual working memory). By blending theory-driven experiments with large-scale electrophysiological recording and circuit-specific causal manipulations in behaving macaques, I aim to uncover how we select relevant information from working memory, and equally important, how we fail to do so when struck by disorders of executive or memory function.

With the growing complexity of functional materials and chemical systems, we often nd ourselves
limited in our ability to fully represent the set of descriptors of a chemical system. In complex chemical
systems, nding a complete crystallographic model that folds all the interatomic correlations using
a small set of structural descriptors may not always be feasible or practical. Alternatively, one can
take a data-driven approach and measure the relative changes in structural or chemical features (e.g,
structural correlations, oxidation states). An experimental data-driven approach does not require
complete models and enjoys the rapidly evolving machine-learning tool-set, which excel at classifying
relational datasets and, if also labelled by an observed property, can provide predictive power that
links system's descriptors with observed properties.
I will focus on two types of complexities:
(1) Hierarchical complexity, in which dierent types of structural or chemical correlations change
change with the probed correlation length. For example, in ferroic materials dierent prop-
erties (e.g., mechanical, dielectric, optoelectronic) may depend dierently on short- and long-
range structural correlations. In multi-component alloys local chemical correlations (random-
distribution, ordering, clustering) can aect corrosion and plasticity, but altogether show a single
average structural phase. Since selected materials' properties depend on correlations at a specic
hierarchical level, it is important to be able to isolate those from one another.
(2) Evolutionary complexity, where the order changes over space and/or time. Nucleation, crys-
tal growth, intercalation - are examples for processes that involve evolutionary complexity and
can also be found in batteries, heterogeneous catalysis and photovoltaics. Isolating and track-
ing order-related correlations in heterogeneous kinetically-stabilized or dynamically changing
systems is, therefore, important for their more complete understanding, design and control.
Total scattering and Pair Distribution Function (PDF) analysis are key methods for unfolding
structural correlations at dierent correlation lengths. Using 4D-STEM to generate nm-resolution
spatially-resolved electron-PDF data taken from hot-rolled Ni-laminated bulk-metallic-glass [1], I
demonstrate how both hierarchical and evolutionary complexity can be uncovered and studied. Par-
tially assisted with a machine-learning classication toolbox, we show how dierent aspects of the
structural and chemical order, such as chemical-short-range-order, can be directly visualized as a
function of position. In a dierent example [2] I show how an evolutionary complex systems can be
manipulated to achieve a desired chemical state. In this example we demonstrate an active reaction
control of Cu redox state from real-time feedback from in-situ synchrotron measurements.
While complexity can lead to a lack of control over a chemical system, it is essentially adding
tuning-knobs that, once isolated, understood and controlled, can unlock new materials with desired
functionalities.
[1] Y. Rakita, et al., Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Mi-
croscopy (SNEM), arXiv:2110.03589 (2021).
[2] Y. Rakita, et al., Active reaction control of Cu redox state based on real-time feedback from in situ synchrotron
measurements, JACS 142, 18758 (2020). DOI: 10.1021/jacs.0c09418.
1

Dopamine regulates multiple brain functions including learning, motivation and movement. Furthermore, the striatum, a major target of dopamine neurons, is parceled into multiple subregions that are associated with different types of behavior, such as Pavlovian, goal-directed, and habitual behaviors. An important question in the field is how dopamine regulates these diverse functions. It has been thought that midbrain dopamine neurons broadcast reward prediction error signals to drive reinforcement learning. However, recent studies have found more diverse dopamine signals than originally thought. How can we reconcile these results? In this talk, I will discuss our recent studies characterizing diverse dopamine signals, and how these findings can be understood in a coherent theoretical framework.
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Aggression and dominance hierarchy are basic social behaviors that are essential for the survival and reproductive success of most mammalian species. Typically, they are displayed whenever conspecifics have to compete for limited resources, such as food, water, territory, or access to mates. As a result, and due to sexual selection, intra-sexual competition is higher in males compared to females as fertile females are a limited resource to males. Thus, males often express a higher level of aggression and are most likely to form a dominance hierarchy in a group. Therefore, most studies of the biological basis of intra-sexual aggression and dominance hierarchy have been focused on males. However, it has long been observed that females also compete with each other and can form dominant hierarchies.
In this study, we aimed to investigate the role of OT+ PVN neurons in the aggression of wild-derived female mice by comparing them to males. Wild-derived mice were chosen for their higher levels of aggression compared to the lab mouse strains, which might have lost these behavioral traits due to artificial selection and socially restricted environment while in captivity.
To manipulate OT+ PVN neurons, we established a wild OT:Cre mouse line by backcrossing wild-derived mice with transgenic lab mice and validated that its phenotype resembles the wild-derived mice. Using these novel wild-backcrossed OT:Cre (Wild-BX) mice, we found that OT+ PVN neurons of females are activated due to agonistic interaction.
Next, we virally ablated, using Casp3, or activated, using DREADD, OT+ PVN neurons in wild-BX males and females, and performed a standard resident-intruder assay (RI) to examine territorial aggression towards same-sex adults and unfamiliar pups. We found that ablation of OT+ PVN neurons in wild-BX females reduces adult and pup-directed aggression and increases sniffing behavior. In contrast, activation of this neuronal population promotes aggressive behavior toward adults and pups and decrees sniffing behavior. In males, similar manipulations did not affect either of these aggressive or sniffing behaviors, except a weak impact on pup-directed aggression.
Moreover, by examining group behavior in a semi-natural environment, we found that ablation of OT+ PVN neurons suppresses dominant hierarchy formation in groups of wild-BX females. In contrast, activation strengthened the hierarchy and increased agonistic behavior in the group. In males, in contrast to the RI, the OT+ PVN ablation delayed the formation of the hierarchy and increased the anxiety in the group, whereas activation weakened the hierarchy and increased pro-social behavior.
These findings suggest that OT PVN neurons have a sexually-dimorphic effect in aggression and dominance hierarchy behaviors, and they emphasize the importance of investigating both sexes in ethologically-relevant animal models and social contexts, in the study of socially relevant neuromodulators.

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