A Compact Representation of Measured BRDFs Using Neural Processes

In this article, we introduce a compact representation for measured BRDFs by leveraging Neural Processes (NPs). Unlike prior methods that express those BRDFs as discrete high-dimensional matrices or tensors, our technique considers measured BRDFs as continuous functions and works in corresponding function spaces . Specifically, provided the evaluations of a set of BRDFs, such as ones in MERL and EPFL datasets, our method learns a low-dimensional latent space as well as a few neural networks to encode and decode these measured BRDFs or new BRDFs into and from this space in a non-linear fashion. Leveraging this latent space and the flexibility offered by the NPs formulation, our encoded BRDFs are highly compact and offer a level of accuracy better than prior methods. We demonstrate the practical usefulness of our approach via two important applications, BRDF compression and editing. Additionally, we design two alternative post-trained decoders to, respectively, achieve better compression ratio for individual BRDFs and enable importance sampling of BRDFs.

Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Common neural functions during children's naturalistic and controlled laboratory mathematics learning

A major goal of human neuroscience is to understand how the brain functions in the real world, and to measure neural processes under conditions that are ecologically valid. A critical step toward this goal is understanding how brain activity during naturalistic tasks that mimic the real world, relates to brain activity in more traditional laboratory tasks. In the present study, we used intersubject correlations to locate reliable stimulus-driven cerebral processes among children and adults in a naturalistic video lesson and a laboratory forced-choice task that shared the same arithmetic concept. We show that relative to a control condition with grammatical content, naturalistic and laboratory arithmetic tasks evoked overlapping activation within brain regions previously associated with math semantics. The regions of specific functional overlap between the naturalistic mathematics lesson and laboratory mathematics task included bilateral intraparietal cortex, which confirms that this region processes mathematical content independently of differences in task mode. These findings suggest that regions of the intraparietal cortex process mathematical content when children are learning about mathematics in the real world.

The neural dynamics associated with computational complexity

Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which participants solved several instances of the 0-1 knapsack problem, a combinatorial optimization problem, while undergoing ultra-high field (7T) functional magnetic resonance imaging (fMRI). Instances varied in two task-independent measures of intrinsic computational hardness: complexity and proof hardness. We characterise a network of brain regions whose activation was correlated with both measures but in distinct ways, including the anterior insula, dorsal anterior cingulate cortex and the intra-parietal sulcus/angular gyrus. Activation and connectivity changed dynamically as a function of complexity and proof hardness, in line with theoretical computational requirements. Overall, our results suggest that computational complexity theory provides a suitable framework to study the effects of computational hardness on the neural processes associated with solving complex cognitive tasks.

Mistimed feeding disrupts circadian rhythms of male mating behavior and female preovulatory LH surges in mice

In rodents, eating at atypical circadian times, such as during the biological rest phase when feeding is normally minimal, reduces fertility. Prior findings suggest this fertility impairment is due, at least in part, to reduced mating success. However, the physiological and behavioral mechanisms underlying this reproductive suppression are not known. In the present study, we tested the hypothesis that mistimed feeding-induced infertility is due to a disruption in the normal circadian timing of mating behavior and/or the generation of pre-ovulatory luteinizing hormone (LH) surges (estrogen positive feedback). In the first experiment, male+female mouse pairs, acclimated to be food restricted to either the light (mistimed feeding) or dark (control feeding) phase, were scored for mounting frequency and ejaculations over 96 hours. Male mounting behavior and ejaculations were distributed much more widely across the day in light-fed mice than in dark-fed controls and fewer light-fed males ejaculated. In the second experiment, the timing of the LH surge, a well characterized circadian event driven by estradiol (E2) and the SCN, was analyzed from serial blood samples taken from ovariectomized and E2-primed female mice that were light-, dark-, or ad-lib-fed. LH concentrations peaked 2h after lights-off in both dark-fed and ad-lib control females, as expected, but not in light-fed females. Instead, the normally clustered LH surges were distributed widely with high inter-mouse variability in the light-fed group. These data indicate that mistimed feeding disrupts the temporal control of the neural processes underlying both ovulation and mating behavior, contributing to subfertility.

Systemic and intra-amygdala administrations of midazolam reverse anxiety-like behavior induced by cohabiting with a cagemate in chronic pain condition

The affective component of pain may be shared among conspecifics through emotional contagion, a form of empathic expression. In this sense, reverberation of negative emotions could generate distress behavioral responses, such as pathological anxiety. Evidences reported that amygdala and its benzodiazepine receptors are involved in perception of pain in others. However, relatively little is known about the neural processes underlying emotional contagion induced by pain observation. In the present study, we investigated the effects of midazolam, an allosteric GABAergic receptor agonist, in anxiety-like behaviors induced by cohabitation with cagemate submitted to sciatic nerve constriction. For this purpose, we administrated systemic (0.5, 1.0 and 2.0 mg/kg) and intra-amygdala midazolam injections (3.0 and 30.0 nmol) in observer cagemates before elevated plus-maze (EPM) evaluation. We found that mice subjected to nerve constriction and their observer cagemates increased anxiety-like behavior in the EPM. Further, systemically (1.0 and 2.0 mg/kg) and intra-amygdala administration of midazolam (3.0 and 30 nmol) reverse this anxiogenic effect. Collectively, these results suggest that social interaction with a cagemate under chronic pain produces anxiety-like responses that could be blocked through midazolam application.

Social Decisions from Description Compared to Experience Rely on Different Cognitive and Neural Processes

Social decisions reveal the degree to which people consider societal needs relative to their own desires. Although many studies showed how social decisions are taken when the consequences of actions are given as explicit information, little is known about how social choices are made when the relevant information was learned through repeated experience. Here, we compared how these two different ways of learning about the value of alternatives (description versus experience) impact social decisions in 147 healthy young adult humans. Using diffusion decision models, we show that, although participants chose similar outcomes across the learning conditions, they sampled and processed information differently. During description decisions, information sampling depended on both chosen and foregone rewards for self and society, while during experience decisions sampling was proportional to chosen outcomes only. Our behavioral data indicate that description choices involved the active processing of more information. Additionally, neuroimaging data from 40 participants showed that the brain activity was more closely associated with the information sampling process during description relative to experience decisions. Overall, our work indicates that the cognitive and neural mechanisms of social decision making depend strongly on how the values of alternatives were learned in addition to individual social preferences.

People with nicotine dependence use-disorder exhibit more prefrontal activity during preparatory control but reduced anterior cingulate activity during reactive control

Reduced inhibitory control and a hypersensitivity to reward are key deficits in drug-dependents, however, they tend to be studied in isolation. Here we seek to understand the neural processes underlying control over reward and how this is different in people with a nicotine use disorder (pNUD). A novel variant of the monetary incentive delay task was performed by pNUD (n = 20) and non-smokers (n = 20), where we added a stop-signal component such that participants had to inhibit prepotent responses to earn a larger monetary reward. Brain activity was recorded using functional magnetic resonance imaging (fMRI). We estimated stop signal reaction times (SSRT), an indicator of impulsivity, and correlated these with brain activity. Inhibitory accuracy scores did not differ between the control group and pNUD. However, pNUD had slower SSRTs, suggesting that they may find it harder to inhibit responses. Brain data revealed that pNUD had greater preparatory control activity in the middle frontal gyrus and inferior frontal gyrus prior to successful inhibitions over reward. In contrast, non-smokers had greater reactive control associated with more activity in the anterior cingulate cortex during these successful inhibitions. SSRT-brain activity correlations revealed that pNUD engaged more control related prefrontal brain regions when SSRTs are slower. Overall, whilst the inhibition accuracy scores were similar between groups, differential neural processes and strategies were used to successfully inhibit a prepotent response. The findings suggest that increasing preparatory control in pNUD may be one possible treatment target in order to increase inhibitory control over reward.

Intermittent brain network reconfigurations and the resistance to social media influence

Since their development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways but it remains unclear how decision making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who show more malleable opinions are characterized by less frequent network reconfigurations while those with more rigid opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between the individuals who are more easily influenced by social media and those who are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that network reconfigurations in the brain may not only be diagnostic to the informational context but also the underlie opinion formation.