Imposing structure on odor representations during learning in the prefrontal cortex

Animals have evolved sensory systems that afford innate and adaptive responses to stimuli in the environment. Innate behaviors are likely to be mediated by hardwired circuits that respond to invariant predictive cues over long periods of evolutionary time. However, most stimuli do not have innate value. Over the lifetime of an animal, learning provides a mechanism for animals to update the predictive value of cues through experience. Sensory systems must therefore generate neuronal representations that are able to acquire value through learning. A fundamental challenge in neuroscience is to understand how and where value is imposed in brain during learning. The olfactory system is an attractive sensory modality to study learning because the anatomical organization is concise in that there are relatively few synapses separating the sense organ from brain areas implicated in learning. Thus, the circuits for learned olfactory behaviors appear to be relatively shallow and therefore more experimentally accessible than other sensory systems. The goal of this thesis is to characterize the representation and function of neural circuits involved in olfactory associative learning. Odor perception is initiated by the binding of odors onto olfactory receptors expressed in the sensory epithelium. Each olfactory receptor neuron (ORN) expresses one of 1500 different receptor genes, the expression of which pushes the ORN to project with spatial specificity onto a defined loci within the olfactory bulb, the olfactory glomeruli. Therefore, each and every odor evokes a stereotyped map of glomerular activity in the bulb. The projection neurons of the olfactory bulb, mitral and tufted (M/T) cells, send axons to higher brain areas, including a significant input to the primary olfactory cortex, the piriform cortex. Axons from M/T cells project diffusely to the piriform without apparent spatial preference; as a consequence, the spatial order of the bulb is discarded in the piriform. In agreement with anatomical data, electrophysiological and optical imaging studies also demonstrate that individual odorants activate sparse subsets of neurons across the piriform without any spatial order. Moreover, individual piriform neurons exhibit discontinuous receptive fields that defy chemical or perceptual categorization. These observations suggests that piriform neurons receive random subsets of glomerular input. Therefore, odor representations in piriform are unlikely to be hardwired to drive specific behaviors. Rather, this model suggests that value must be imposed upon the piriform through learning. Indeed, the piriform has been shown to be both sufficient and necessary for aversive olfactory learning without affecting innate odor responses. However, how value is imposed on odor representations in the piriform and downstream associational areas remain largely unknown. We first developed a strategy to track neural activity in a population of neurons across multiple days in deep brain areas using 2-photon endoscopic imaging. This allowed us to assay changes in neural responses to odors during learning in piriform and in downstream associative areas. Using this technique, we first observe that piriform odor responses are unaffected by learning, so learning must therefore impose discernable changes in neural activity downstream of piriform. Piriform projects to multiple downstream areas that are implicated in appetitive associative learning, such as the orbitofrontal cortex (OFC). Imaging of neural activity in the OFC reveal that OFC neurons acquire strong responses to conditioned odors (CS+) during learning. Moreover, multiple and distinct CS+ odors activatethe same population of OFC neurons, and these responses are gated by context and internal state. Together, our imaging data shows that an external and sensory representation in the piriform is transformed into an internal and cognitive representation of value in the OFC. Moreover, we found that optogenetic silencing of the OFC impaired the ability of mice to acquire learned associations. Therefore, the robust representation of expected value of the odor cues is necessary for the formation of appetitive associations. We made an important observation: once the task has been learned with a set of odors, the OFC representation decays after learning has plateaued and remains silent even when mice encounter novel odors they haven’t previously experienced. Moreover, silencing the OFC when it was not actively engaged during the subsequent learning of new odors had no effect on learning. These sets of imaging and silencing experiments reveal that the OFC is only important during initial learning; once task structure has been acquired, it is no longer needed. Task performance after initial task acquisition must therefore be accommodated by other brain regions that can store the learned association for long durations. We therefore searched for other brain regions that held learned associations long-term. In the medial prefrontal cortex (mPFC), we observe that the learned representation persists throughout the entire course of training. Unlike the OFC, not only does this representation encode the positive expected value of CS+ odors, it also encodes the negative expected value of CS- odors in a non-overlapping ensemble of neurons. We further show through optogenetic silencing that this representation is necessary for task performance after the task structure has already been acquired. Therefore, while the OFC representation is required for initial task acquisition, the mPFC representation is required for subsequent appetitive learning and performance. Why would a learned representation vanish in the OFC and betransfered elsewhere? We hypothesize that the brain may allocate a portion of its real estate to be a cognitive playground where experimentation and hypothesis testing takes place. Once this area solves a task, it may unload what it has learned to storage units located elsewhere to free up space to learn new tasks. We further imaged another associative area, the basolateral amygdala (BLA), and found a representation of positive value that appears to be generated from a Hebbian learning mechanism. However, the silencing of this representation during learning had no effect. This suggests that while multiple and distributed brain areas encode cues that predict the reward, not all may be necessary for the learning process or for task performance. In summary, we have described a series of experiments that map the representation and function of different associational areas that underlie learning. The data and the techniques employed have the potential to significantly advance the understanding of learned behavior

Relational learning for set value functions

Relational learning is learning in a context where we have a set of items with relationships. For example, in a recommender system or advertising platform, items are grouped into lists to attract user attention, and some items may be more popular than others. We are often interested in learning individual abilities, approximating group performances and making best set selection. However, it could be challenging as we have limited feedback and various uncertainties. We might only observe noisy aggregate feedback at the set level (set level randomness), and each item could be a random variable following some distributions (item level randomness). To tackle the problem, we model the group performance using a set value function, defined as a function of item values within the group of interest. We first study the beta model for hypergraphs. The model treats relational data as hypergraphs where nodes represent items and hyper-edges group items into sets. The goal is to estimate individual beta values from the group outcomes. We study the inference problem under different settings using maximum likelihood estimation (MLE). We move on to consider more general set value functions and the second source of randomness at the item level. The goal is to find good item representations (sketches) for approximation of stochastic valuation functions, defined as the expectation of set value functions of independent random variables. We present an approximation everywhere guarantee for a wide range of stochastic valuation functions. Finally, we study an online variant where an agent can draw samples sequentially. At each time step, the agent chooses a group of items subject to constraints and receives some form of feedbacks. The goal is to select a set of items with maximum performances according to some stochastic valuation functions. We consider the regret minimization setting and address the problem under value-index feedback

Robust Parameter Design of Functional Responses Based on Bayesian SUR Models

As for the robust parameter design of functional responses, a Bayesian Seemingly Unrelated Regression (SUR) model is proposed to take into account the model uncertainty and response variability in this paper. First of all, the SUR model is used to build the functional relationship between the output responses and the input factors at different time points. Also, Bayesian analysis of the SUR model is performed to consider the influence of the model parameter uncertainty on the research results. Secondly, the process means and variances of the functional responses at different time points are estimated by the posterior samples of the simulated responses. Moreover, an integrated performance index (i.e. mean square error) is establish by using the above process means and variances. Then, the optimal parameter settings may be found by minimizing the MSE performance index. Finally, the advantages of the proposed method are illustrated by an example from the literature

Gate-induced insulator to band-like transport transition in organolead halide perovskite

Understanding the intrinsic charge transport in organolead halide perovskites is essential for the development of high-efficiency photovoltaics and other optoelectronic devices. Despite the rapid advancement of the organolead halide perovskite in photovoltaic and optoelectronic applications, the intrinsic charge carrier transport in these materials remains elusive partly due to the difficulty of fabricating electrical devices and obtaining good electrical contact. Here, we report the fabrication of organolead halide perovskite microplates with monolayer graphene as low barrier electrical contact. A systematic charge transport studies reveal an insulator to band-like transport transition. Our studies indicate that the insulator to band-like transport transition depends on the orthorhombic-to-tetragonal phase transition temperature and defect densities of the organolead halide perovskite microplates. Our findings are not only important for the fundamental understanding of charge transport behavior but also offer valuable practical implications for photovoltaics and optoelectronic applications based on the organolead halide perovskite.Comment: 18 pages, 5 figure

A Framing Link Based Tabu Search Algorithm for Large-Scale Multidepot Vehicle Routing Problems

A framing link (FL) based tabu search algorithm is proposed in this paper for a large-scale multidepot vehicle routing problem (LSMDVRP). Framing links are generated during continuous great optimization of current solutions and then taken as skeletons so as to improve optimal seeking ability, speed up the process of optimization, and obtain better results. Based on the comparison between pre- and postmutation routes in the current solution, different parts are extracted. In the current optimization period, links involved in the optimal solution are regarded as candidates to the FL base. Multiple optimization periods exist in the whole algorithm, and there are several potential FLs in each period. If the update condition is satisfied, the FL base is updated, new FLs are added into the current route, and the next period starts. Through adjusting the borderline of multidepot sharing area with dynamic parameters, the authors define candidate selection principles for three kinds of customer connections, respectively. Link split and the roulette approach are employed to choose FLs. 18 LSMDVRP instances in three groups are studied and new optimal solution values for nine of them are obtained, with higher computation speed and reliability

Signal identification with Kalman Filter towards background-free neutrinoless double beta decay searches in gaseous detectors

Particle tracks and differential energy loss measured in high pressure gaseous detectors can be exploited for event identification in neutrinoless double beta decay~($0\nu \beta \beta$) searches. We develop a new method based on Kalman Filter in a Bayesian formalism (KFB) to reconstruct meandering tracks of MeV-scale electrons. With simulation data, we compare the signal and background discrimination power of the KFB method assuming different detector granularities and energy resolutions. Typical background from $^{232}$Th and $^{238}$U decay chains can be suppressed by another order of magnitude than that in published literatures, approaching the background-free regime. For the proposed PandaX-III experiment, the $0\nu \beta \beta$ search half-life sensitivity at the 90\% confidence level would reach $2.7 \times 10^{26}$~yr with 5-year live time, a factor of 2.7 improvement over the initial design target

Global evaluation of key factors influencing nitrogen fertilization efficiency in wheat: a recent meta-analysis (2000-2022)

Improving nitrogen use efficiency (NUE) without compromising yield remains a crucial agroecological challenge in theory and practice. Some meta-analyses conducted in recent years investigated the impact of nitrogen (N) fertilizer on crop yield and gaseous emissions, but most are region-specific and focused on N sources and application methods. However, various factors affecting yield and N fertilizer efficiency in wheat crops on a global scale are not extensively studied, thus highlighting the need for a comprehensive meta-analysis. Using 109 peer-reviewed research studies (published between 2000 and 2022) from 156 experimental sites (covering 36.8, 38.6 and 24.6% of coarse, medium, and fine texture soils, respectively), we conducted a global meta-analysis to elucidate suitable N management practices and the key factors influencing N fertilization efficiency in wheat as a function of yield and recovery efficiency and also explained future perspectives for efficient N management in wheat crop. Overall, N fertilization had a significant impact on wheat yield. A curvilinear relationship was found between N rates and grain yield, whereas maximum yield improvement was illustrated at 150-300 kg N ha-1. In addition, N increased yield by 92.18% under direct soil incorporation, 87.55% under combined chemical and organic fertilizers application, and 72.86% under split application. Site-specific covariates (climatic conditions and soil properties) had a pronounced impact on N fertilization efficiency. A significantly higher yield response was observed in regions with MAP > 800 mm, and where MAT remained < 15 °C. Additionally, the highest yield response was observed with initial AN, AP and AK concentrations at < 20, < 10 and 100-150 mg kg-1, respectively, and yield response considerably declined with increasing these threshold values. Nevertheless, regression analysis revealed a declining trend in N recovery efficiency (REN) and the addition of N in already fertile soils may affect plant uptake and RE. Global REN in wheat remained at 49.78% and followed a negative trend with the further increase of N supply and improvement in soil properties. Finally, an advanced N management approach such as “root zone targeted fertilization” is suggested to reduce fertilizer application rate and save time and labor costs while achieving high yield and NUE

Layer-by-Layer Degradation of Methylammonium Lead Tri-iodide Perovskite Microplates

The methylammonium lead iodide (MAPbI3) perovskite has attracted considerable interest for its high-efficiency, low-cost solar cells, but is currently plagued by its poor environmental and thermal stability. To aid the development of robust devices, we investigate here the microscopic degradation pathways of MAPbI3 microplates. Using in situ transmission electron microscopy to follow the thermal degradation process, we find that under moderate heating at 85°C the crystalline structure shows a gradual evolution from tetragonal MAPbI3 to trigonal lead iodide layered crystals with a fixed crystallographic direction. Our solid-state nudged elastic band calculations confirm that the surface-initiated layer-by-layer degradation path exhibits the lowest energy barrier for crystal transition. We further show experimentally and theoretically that encapsulation of the perovskites with boron nitride flakes suppresses the surface degradation, greatly improving its thermal stability. These studies provide mechanistic insight into the thermal stability of perovskites that suggests new designs for improved stability