Strong Aversive Conditioning Triggers a Long-Lasting Generalized Aversion

Generalization is an adaptive mnemonic process in which an animal can leverage past learning experiences to navigate future scenarios, but overgeneralization is a hallmark feature of anxiety disorders. Therefore, understanding the synaptic plasticity mechanisms that govern memory generalization and its persistence is an important goal. Here, we demonstrate that strong CTA conditioning results in a long-lasting generalized aversion that persists for at least two weeks. Using brain slice electrophysiology and activity-dependent labeling of the conditioning-active neuronal ensemble within the gustatory cortex, we find that strong CTA conditioning induces a long-lasting increase in synaptic strengths that occurs uniformly across superficial and deep layers of GC. Repeated exposure to salt, the generalized tastant, causes a rapid attenuation of the generalized aversion that correlates with a reversal of the CTA-induced increases in synaptic strength. Unlike the uniform strengthening that happens across layers, reversal of the generalized aversion results in a more pronounced depression of synaptic strengths in superficial layers. Finally, the generalized aversion and its reversal do not impact the acquisition and maintenance of the aversion to the conditioned tastant (saccharin). The strong correlation between the generalized aversion and synaptic strengthening, and the reversal of both in superficial layers by repeated salt exposure, strongly suggests that the synaptic changes in superficial layers contribute to the formation and reversal of the generalized aversion. In contrast, the persistence of synaptic strengthening in deep layers correlates with the persistence of CTA. Taken together, our data suggest that layer-specific synaptic plasticity mechanisms separately govern the persistence and generalization of CTA memory.

Effect of electrochemical modification of titanium hydride fraction on oxygen content in surface and deep layers

The paper presents a study of the microstructure and oxygen concentration in the surface and deep layers of fractions of unmodified titanium hydride and titanium hydride modified by electrodeposited layers of Ti and Cu at temperatures of 300-900 ° C. The composition of the oxide layer and the concentration of titanium and oxygen atoms are estimated. It is shown that an increase in the thickness and compaction of the oxide layer with increasing temperature prevents the penetration of oxygen into the deep layers of the unmodified fraction of titanium hydride. Modification of titanium hydride by electrochemical deposition of metallic titanium at a temperature of 700 °C reduces the oxygen concentration in titanium hydride at a layer depth of 50 μm from 35 wt% to 12.5 wt%. Electrodeposition of coatings based on titanium and copper at 700 °C reduces the oxygen concentration to 9.2 wt%, which may be due to the protective mechanism of the formed copper titanate layer. At 900 °C, in the modification layer based on titanium and copper, due to the eutectoid transformation of the β-phase of titanium, the process of contact melting occurs and a multiphase zone is formed. The oxygen concentration at a layer depth of 50 μm is no more than 12.4 wt%.

Application of Artificial Intelligence in Tesla- A Case Study

Background/Purpose: Artificial intelligence algorithms are like humans, performing a task repeatedly, each time changing it slightly to maximize the result. A neural network is made up of several deep layers that allow for learning. Financial services, ICT, life science, oil and gas, retail, automotive, industrial healthcare, and chemicals and manufacturing sectors are among the industries that employ these algorithms. The electric motor is a new concept, and the automobile industry is now undergoing intensive research to determine whether it is practicable and financially viable. There are already some first movers, such as Tesla, who have successfully established their model and are moving forward. Tesla is forcing the auto industry to adapt quickly. Tesla introduced Autopilot driver capability for its Model S vehicle. Tesla Autopilot is a suite of sophisticated driver-assist technologies that include traffic adjustment, congested roads navigation system, autopilot car-parks, computer-controlled road rules, semi-autonomous route planning on major roadways, and the ability to summon the vehicle out of a designated car-park. This article provides a comprehensive analysis of Tesla Company and Innovations of Autopilot Vehicles. Objective: This case study report addresses the growth of Tesla Company in the field of Autonomous Vehicles. Design/Methodology/Approach: The knowledge for this case study of Tesla was gathered from various academic articles, online articles, and the SWOT framework. Findings/Result: Based on the research, this paper discusses the technological histories, Autopilot driving features, safety concerns, financial plans, market challenges, different models, and how Tesla Inc. is accelerating the world's movement in multiple initiatives such as the contribution of the global economic system, study in the Artificial Intelligence and Machine Learning area. Originality/Value: This paper study provides a brief overview of Tesla Inc. given the various data collected, and information about Tesla Autopilot vehicles using Artificial Intelligence based Innovations in Entrepreneurial Oriented Cars. Paper type: A Research Case study paper - focuses on Application of Artificial Intelligence in Tesla Autopilot Vehicles and growth & Journey of the Tesla Inc. Company.

Rostral and caudal BLA engage distinct circuits in the prelimbic and infralimbic PFC

Connections from the basolateral amygdala (BLA) to medial prefrontal cortex (PFC) regulate memory and emotion and become disrupted in neuropsychiatric disorders. We hypothesized that the diverse roles attributed to interactions between the BLA and PFC reflect multiple circuits nested within a wider network. To assess these circuits, we first used anatomy to show that the rostral BLA (rBLA) and caudal BLA (cBLA) differentially project to prelimbic (PL) and infralimbic (IL) subregions of the PFC, respectively. We then combined in vivo silicon probe recordings and optogenetics to show that rBLA primarily engages PL, whereas cBLA mainly influences IL. Using ex vivo whole-cell recordings and optogenetics, we then assessed which neuronal subtypes are targeted, showing that rBLA preferentially drives layer 2 (L2) cortico-amygdalar (CA) neurons in PL, whereas cBLA drives layer 5 (L5) pyramidal tract (PT) cells in IL. Lastly, we used soma-tagged optogenetics to explore the local circuits linking superficial and deep layers of PL, showing how rBLA can also impact L5 PT cells. Together, our findings delineate how subregions of the BLA target distinct networks within the PFC to have different influence on prefrontal output.

Temporally precise movement-based predictions in the mouse auditory cortex

Many of the sensations experienced by an organism are caused by their own actions, and accurately anticipating both the sensory features and timing of self-generated stimuli is crucial to a variety of behaviors. In the auditory cortex, neural responses to self-generated sounds exhibit frequency-specific suppression, suggesting that movement-based predictions may be implemented early in sensory processing. Yet it remains unknown whether this modulation results from a behaviorally specific and temporally precise prediction, nor is it known whether corresponding expectation signals are present locally in the auditory cortex. To address these questions, we trained mice to expect the precisely timed acoustic outcome of a forelimb movement using a closed-loop sound-generating lever. Dense neuronal recordings in the auditory cortex revealed suppression of responses to self-generated sounds that was specific to the expected acoustic features, specific to a precise time within the movement, and specific to the movement that was coupled to sound during training. Predictive suppression was concentrated in L2/3 and L5, where deviations from expectation also recruited a population of prediction-error neurons that was otherwise unresponsive. Recording in the absence of sound revealed abundant movement signals in deep layers that were biased toward neurons tuned to the expected sound, as well as temporal expectation signals that were present throughout the cortex and peaked at the time of expected auditory feedback. Together, these findings reveal that predictive processing in the mouse auditory cortex is consistent with a learned internal model linking a specific action to its temporally precise acoustic outcome, while identifying distinct populations of neurons that anticipate expected stimuli and differentially process expected versus unexpected outcomes.

A note about density staircases in the Gulf of Naples : 20 years of persistent weak salt-fingering layers in a coastal area

This is a short communication about the inter-annual recurring presence at the coastal site in the Gulf of Naples of density staircases visible below the mixed surface layer of the water-column, from the end of summer to the beginning of winter, each year during nearly two decades of survey (2001 to 2020). We repetitively observe sequences from 1 to 4 small vertical staircases structures (~ 3 m thick) in the density profiles (~ Δ0.2 kg.m-3), located between 10 m to 50 m deep below the seasonal mixed layer depth. We interpret these vertical structures as the result of double diffusive processes that could host salt-fingering regime (SF) due to warm salty water parcels overlying on relatively fresher and colder layers. This common feature of the Mediterranean basin (i.e., the thermohaline staircases of the Tyrrhenian sea) may sign here for the lateral intrusions of nearshore water masses. These stably stratified layers are characterized by density ratio Rρ 5.0 to 10.0, slightly higher than the critical range (1.0 - 3.0) generally expected for fully developed salt-fingers. SF mixing, such as parameterized (Zhang et al., 1998), appears to inhibit weakly the effective eddy diffusivity with negative averaged value (~ - 1e-8 m2.s-1). A quasi 5-year cycle is visible in the inter-annual variability of the eddy diffusivity associated to SF, suggesting a decadal modulation of the parameters regulating the SF regime. Even contributing weakly to the turbulent mixing of the area, we hypothesis that SF could influence the seasonal stratification by intensifying the density of deep layers. Downward transfer of salt could have an impact on the nutrient supply for the biological communities, that remains to be determined.

Pop-out search instigates beta-gated feature selectivity enhancement across V4 layers

Visual search is a workhorse for investigating how attention interacts with processing of sensory information. Attentional selection has been linked to altered cortical sensory responses and feature preferences (i.e., tuning). However, attentional modulation of feature selectivity during search is largely unexplored. Here we map the spatiotemporal profile of feature selectivity during singleton search. Monkeys performed a search where a pop-out feature determined the target of attention. We recorded laminar neural responses from visual area V4. We first identified “feature columns” which showed preference for individual colors. In the unattended condition, feature columns were significantly more selective in superficial relative to middle and deep layers. Attending a stimulus increased selectivity in all layers but not equally. Feature selectivity increased most in the deep layers, leading to higher selectivity in extragranular layers as compared to the middle layer. This attention-induced enhancement was rhythmically gated in phase with the beta-band local field potential. Beta power dominated both extragranular laminar compartments, but current source density analysis pointed to an origin in superficial layers, specifically. While beta-band power was present regardless of attentional state, feature selectivity was only gated by beta in the attended condition. Neither the beta oscillation nor its gating of feature selectivity varied with microsaccade production. Importantly, beta modulation of neural activity predicted response times, suggesting a direct link between attentional gating and behavioral output. Together, these findings suggest beta-range synaptic activation in V4’s superficial layers rhythmically gates attentional enhancement of feature tuning in a way that affects the speed of attentional selection.

Change of Retinal Vessel Density After Lowering Intraocular Pressure in Ocular Hypertension

Purpose: To investigate the relationship between retinal microvasculature changes and intraocular pressure (IOP) for ocular hypertension (OHT) patients and further assess the factors associated with retinal microcirculation changes.Methods: This was a single-center prospective study designed for OHT patients, which consisted of two visits. After collecting baseline data of those who met the eligibility criteria, these patients were treated with latanoprost 0.005% ophthalmic solution for 4 weeks. Peripapillary vessel density (VD) of radial peripapillary capillaries (RPC) layer, macular VD in both superficial and deep layers, and foveal avascular zone (FAZ) area were measured by optical coherence tomography angiography (OCTA) before and after the treatment. We compared the changes in IOP and VD among the two visits by paired-sample t-test. Bonferroni correction was applied. Factors associated with VD changes were analyzed by linear regression analysis.Results: Thirty-four eyes of thirty-four patients were included. The mean IOP decreased by 6.5 ± 2.2 mmHg (p < 0.001). The peripapillary RPC VD increased significantly from 51.8 ± 2.5 to 53.0 ± 3.1% (Adjusted-p = 0.012). We found no significant difference in detailed sectors of the peripapillary region after correction. In the macular area, both the superficial and deep layers in foveal (superficial: 0.2 ± 1.9%, p = 0.523; deep: 0.0 ± 2.3%, p = 0.969) and parafoveal (superficial: 0.3 ± 3.0%, p = 0.565; deep: 0.5 ± 3.1%, p = 0.423) VD remained unchanged. The decrease of the mean FAZ area was insignificant (p = 0.295). The percentage of IOP reduction (β = 0.330, p = 0.031) and the baseline RNFL thickness (β = 0.450, p = 0.004) significantly correlated with the percentage of peripapillary RPC VD improvement in the multivariate linear regression analysis.Conclusion: The peripapillary VD in OHT patients increased after the reduction of IOP. The mild change of IOP did not alter the microcirculation in the macula. In addition, the percentage of IOP change and the baseline RNFL thickness were independent factors for the peripapillary RPC VD improvement.

Layered Feature Representation for Differentiable Architecture Search

Differentiable architecture search (DARTS) approach has made great progress in reducing the com- putational costs of neural architecture search. DARTS tries to discover an optimal architecture module called cell from a predefined super network. However, the obtained cell is then repeatedly and simply stacked to build a target network, failing to extract layered fea- tures hidden in different network depths. Therefore, this target network cannot meet the requirements of prac- tical applications. To address this problem, we propose an effective approach called Layered Feature Repre- sentation for Differentiable Architecture Search (LFR- DARTS). Specifically, we iteratively search for multiple cells with different architectures from shallow to deep layers of the super network. For each iteration, we optimize the architecture of a cell by gradient descent and prune out weak connections from this cell. After obtain- ing the optimal architecture of this cell, we deepen the super network by increasing the number of this cell, so as to create an adaptive network context to search for a deeper-adaptive cell in the next iteration. Thus, our LFR-DARTS can discover the architecture of each cell at a specific and adaptive network depth, which embeds the ability of layered feature representations into each cell to sufficiently extract layered features in different depths. Extensive experiments show that our algorithm achieves an advanced performance on the datasets of CIFAR10, fashionMNIST and ImageNet while at low search costs.