Functional Differences in the Backward Shifts of CA1 and CA3 Place Fields in Novel and Familiar Environments

Insight into the processing dynamics and other neurophysiological properties of different hippocampal subfields is critically important for understanding hippocampal function. In this study, we compared shifts in the center of mass (COM) of CA3 and CA1 place fields in a familiar and completely novel environment. Place fields in CA1 and CA3 were simultaneously recorded as rats ran along a closed loop track in a familiar room followed by a session in a completely novel room. This process was repeated each day over a 4-day period. CA3 place fields shifted backward (opposite to the direction of motion of the rat) only in novel environments. This backward shift gradually diminished across days, as the novel environment became more familiar with repeated exposures. Conversely, CA1 place fields shifted backward across all days in both familiar and novel environments. Prior studies demonstrated that CA1 place fields on average do not exhibit a backward shift during the first exposure to an environment in which the familiar cues are rearranged into a novel configuration, although CA3 place fields showed a strong backward shift. Under the completely novel conditions of the present study, no dissociation was observed between CA3 and CA1 during the first novel session (although a strong dissociation was observed in the familiar sessions and the later novel sessions). In summary, this is the first study to use simultaneous recordings in CA1 and CA3 to compare place field COM shift and other associated properties in truly novel and familiar environments. This study further demonstrates functional differentiation between CA1 and CA3 as the plasticity of CA1 place fields is affected differently by exposure to a completely novel environment in comparison to an altered, familiar environment, whereas the plasticity of CA3 place fields is affected similarly during both types of environmental novelty

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Finding the engram.

Many attempts have been made to localize the physical trace of a memory, or engram, in the brain. However, until recently, engrams have remained largely elusive. In this Review, we develop four defining criteria that enable us to critically assess the recent progress that has been made towards finding the engram. Recent \u27capture\u27 studies use novel approaches to tag populations of neurons that are active during memory encoding, thereby allowing these engram-associated neurons to be manipulated at later times. We propose that findings from these capture studies represent considerable progress in allowing us to observe, erase and express the engram

New technologies for examining neuronal ensembles in drug addiction and fear

Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. Additionally, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches—Daun02 inactivation, FACS sorting of activated neurons and c-fos-GFP transgenic rats — that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools — c-fos-tTA mice and inactivation of CREB-overexpressing neurons — that have been used to study the role of neuronal ensembles in conditioned fear

3D bioactive composite scaffolds for bone tissue engineering

Bone is the second most commonly transplanted tissue worldwide, with over four million operations using bone grafts or bone substitute materials annually to treat bone defects. However, significant limitations affect current treatment options and clinical demand for bone grafts continues to rise due to conditions such as trauma, cancer, infection and arthritis. Developing bioactive three-dimensional (3D) scaffolds to support bone regeneration has therefore become a key area of focus within bone tissue engineering (BTE). A variety of materials and manufacturing methods including 3D printing have been used to create novel alternatives to traditional bone grafts. However, individual groups of materials including polymers, ceramics and hydrogels have been unable to fully replicate the properties of bone when used alone. Favourable material properties can be combined and bioactivity improved when groups of materials are used together in composite 3D scaffolds. This review will therefore consider the ideal properties of bioactive composite 3D scaffolds and examine recent use of polymers, hydrogels, metals, ceramics and bio-glasses in BTE. Scaffold fabrication methodology, mechanical performance, biocompatibility, bioactivity, and potential clinical translations will be discussed

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at \sqrt{s}=13 TeV

This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 \hbox {fb}^{-1} of pp collision data at \sqrt{s}=13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of Z\rightarrow \mu \mu and J/\psi \rightarrow \mu \mu decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of |\eta |<2.7

Measurements of differential cross-sections in top-quark pair events with a high transverse momentum top quark and limits on beyond the Standard Model contributions to top-quark pair production with the ATLAS detector at √s = 13 TeV

Cross-section measurements of top-quark pair production where the hadronically decaying top quark has transverse momentum greater than 355 GeV and the other top quark decays into ℓνb are presented using 139 fb−1 of data collected by the ATLAS experiment during proton-proton collisions at the LHC. The fiducial cross-section at s = 13 TeV is measured to be σ = 1.267 ± 0.005 ± 0.053 pb, where the uncertainties reflect the limited number of data events and the systematic uncertainties, giving a total uncertainty of 4.2%. The cross-section is measured differentially as a function of variables characterising the tt¯ system and additional radiation in the events. The results are compared with various Monte Carlo generators, including comparisons where the generators are reweighted to match a parton-level calculation at next-to-next-to-leading order. The reweighting improves the agreement between data and theory. The measured distribution of the top-quark transverse momentum is used to search for new physics in the context of the effective field theory framework. No significant deviation from the Standard Model is observed and limits are set on the Wilson coefficients of the dimension-six operators OtG and Otq(8), where the limits on the latter are the most stringent to date. [Figure not available: see fulltext.]

Search for bottom-squark pair production in pp collision events at √s=13 TeV with hadronically decaying τ-leptons, b-jets, and missing transverse momentum using the ATLAS detector

A search for pair production of bottom squarks in events with hadronically decaying τ -leptons, b -tagged jets, and large missing transverse momentum is presented. The analyzed dataset is based on proton-proton collisions at √ s = 13     TeV delivered by the Large Hadron Collider and recorded by the ATLAS detector from 2015 to 2018, and corresponds to an integrated luminosity of 139     fb − 1 . The observed data are compatible with the expected Standard Model background. Results are interpreted in a simplified model where each bottom squark is assumed to decay into the second-lightest neutralino ˜ χ 0 2 and a bottom quark, with ˜ χ 0 2 decaying into a Higgs boson and the lightest neutralino ˜ χ 0 1 . The search focuses on final states where at least one Higgs boson decays into a pair of hadronically decaying τ -leptons. This allows the acceptance and thus the sensitivity to be significantly improved relative to the previous results at low masses of the ˜ χ 0 2 , where bottom-squark masses up to 850 GeV are excluded at the 95% confidence level, assuming a mass difference of 130 GeV between ˜ χ 0 2 and ˜ χ 0 1 . Model-independent upper limits are also set on the cross section of processes beyond the Standard Model

Dijet Resonance Search with Weak Supervision Using root S=13 TeV pp Collisions in the ATLAS Detector

This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A → BC, for mA ∼ OðTeVÞ, mB; mC ∼ Oð100 GeVÞ and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 ffiffi s p ¼ 13 TeV pp collision dataset of 139 fb−1 recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with mA, mB, and mC. For example, when mA ¼ 3 TeV and mB ≳ 200 GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on mC. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model boson