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A subset of megathrust earthquakes produce anomalously large tsunamis for their magnitude. All of these recorded ‘tsunami earthquakes’ in the past 50 years had extensional aftershocks in the upper plate. These include the two largest and most destructive earthquakes of that period, the 2004 Sumatra–Andaman and the 2011 Tohoku events. Evidence from the region of Tohoku indicates that normal fault slip in the upper plate during the earthquake may have contributed to the tsunami size. Here we present a numerical model that shows how a reduction of the dip of a subducting slab, on a timescale of millions of years, can result in an extensional fault failure above a megathrust earthquake on timescales of seconds to months. Slab dip reduction bends the upper plate so that the shallow part fails in extension when a megathrust rupture relieves compressional stress. This results in a distribution of extensional aftershocks comparable to that seen above the Tohoku megathrust. Volcanic arc migra- tion and uplift data for Tohoku and several other tsunami earthquakes is consistent with slab dip reduction. The collection of more such data might identify other areas of tsunami hazard related to slab dip reduction.

The question of how to measure a smell has troubled scientists for over a century. It was none other than Alexander Graham Bell that raised the challenge: "we have very many different kinds of smells, all the way from the odor of violets and roses up to asafoetida. But until you can measure their likenesses and differences you can have no science of odor”. Such a measure of smell can be naturally derived from a model of olfactory perceptual quality space, and several such models have recently been put forth. These typically rely on finding mathematical rules that link odorant structure to aspects of odor perception.
Here, I collected 49,788 perceptual odor estimates from 199 participants, and built such a model, finalizing a physicochemical measure of smell. This measure, expressed in radians, predicts real-world odorant pairwise perceptual similarity from odorant structure alone. Using this measure, I met Bell's challenge by accurately predicting the perceptual similarity of rose, violet and asafoetida, from their physicochemical structure. Next, based on thousands of comparisons, I identified a cutoff in this measure, below 0.05 radians, where discrimination between pairs of mixtures becomes highly challenging. To assess the usefulness of this measure, I investigated whether it can be used to create olfactory metamers, namely non-overlapping molecular compositions that share a common percept. Characterizing the link between physical structure and ensuing perception in vision and audition, and the creation of perceptual entities such as metamers, was important towards understanding their underlying dimensionality, brain mechanisms, and towards their ultimate digitization. I suggest that olfactory metamers can similarly aid these goals in olfaction.

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Meeting ID: 933 6083 6031
Password: 591230

Hydrological systems are inherently non-linear and exhibit an enormous structural and functional heterogeneity. Strikingly, we can nevertheless successfully simulate stream flow generation and the water balance of river catchments with rather simple models that are largely incompatible with the frequently reported subscale process heterogeneity and non-linearity. Here we argue that subscale structural heterogeneity and randomness must not prevent the emergence of functional simplicity. On the contrary, we found simplicity to emerge at rather small scales, reflecting self-organization in hydrological functioning not despite but due to subscale small-scale heterogeneity and the dissipative nature of hydrological process. While we acknowledge that hydrological landscapes are heterogeneous, they are by no means a random product. Catchments exhibit a considerable spatial organisation, which manifests through structured patterns of topography, soil, vegetation, self-similar surface and subsurface drainage networks and most prominently through ubiquitous preferential flow phenomena. While this organized “catchment from” does strongly determine present storage, cycling and release of water, energy and chemical species, this catchment form has in turn been shaped by of water, energy, and nutrients of the past. Is this “co-evolution” just chance or manifested self-organization? This question has been inspiring many scientists to search for thermodynamic principles that link form and function in the Earth system. Here we will present evidence that a thermodynamic and information theoretic perspective opens up new avenues for (i) diagnosing and explaining self-organization in hydrological dynamics, (i) upscaling of constituting relations and (i) using thermodynamic optimality for hydrological predictions.

One of the most fundamental concepts in theoretical neuroscience is that of an attractor neural network, in which recurrent synaptic connectivity constraints the joint activity of neurons into a highly restricted repertoire of population activity patterns. In continuous attractor networks, these activity patterns span a continuous, low-dimensional manifold. I will survey two recent works from my group that are related to this concept. The first work is concerned with fixational eye drifts, a form of eye motion that occurs between saccades and is characterized by smooth, yet random, diffusive-like motion. This motion is tiny compared to saccadic eye motion, yet it is highly consequential for high-acuity vision. Even though fixational drift has been identified at least as early as the 19th century, its mechanistic origins have remained completely unknown. We hypothesize that the main drive for fixational drifts arises in diffusive motion along a line-attractor memory network - the oculomotor network, which is responsible for maintaining a fixed activation of the ocular muscles between saccades. I will present evidence in support of this hypothesis, coming from electrophysiology in monkeys and from theoretical modeling. The second work is concerned with the ability of a single recurrent neural network to express activity patterns that span multiple yet distinct continuous manifolds, a question that has been of interest in the context of spatial coding, across multiple environments, in area CA3 of the hippocampus.


Zoom link to join-
https://weizmann.zoom.us/j/96608033618?pwd=SEdJUkR2ZzRBZ3laUUdGbWR1VFJTdz09

Meeting ID: 966 0803 3618
Password: 564068
Host: Dr. Rita Schmidt rita.schmidt@weizmann.ac.il tel: 9070

NASA's Juno Mission is now completing its 5 year nominal mission around Jupiter, orbiting the planet in an eccentric polar-orbit every 53 days. One of the prime mission objectives is better understanding the atmospheric dynamics through gravitational, microwave, infrared and magnetic measurements. In this talk, we will focus on three new results explaining different aspects of the dynamics on Jupiter. First, infrared imaging data revealed that Jupiter’s poles are surrounded by 5 cyclones around the North Pole and 8 cyclones around the South Pole. We explain the location, size and stability of these circumpolar cyclones based on vorticity dynamics. Second, using microwave data, revealing Jupiter’s deep ammonia abundance structure, we show that Jupiter has 8 meridional circulation cells in each hemisphere. These cells resemble in their governing physics Earth's midlatitude Ferrel cells, and relate to the observed red and white belts and zones at Jupiter’s cloud-level. Finally, using Juno’s gravity measurements we constrain the depth of Jupiter’s east-west jet-streams, and the depth (mass) of the most iconic vortex in the Solar system — Jupiter’s Great Red Spot. Overall, this unique multiple instrument dataset allows now explaining the governing physics of several outstanding aspects of Jupiter’s internal and atmospheric dynamics. We will also compare the dynamics to those of Saturn, generalizing some of the this new understanding.

Zoom Link: https://weizmann.zoom.us/j/97324532197?pwd=MGoxSGhJODNWQ2ZGT1p4elJjMG9lZz09

Cell-cell communication is essential for growth, development and function. Cells can communicate mechanically by responding to mechanical deformations generated by their neighbors in the extracellular matrix (ECM). The ECM is a non-linear viscoelastic material and therefore mechanical communication is expected to be frequency-dependent. In my talk, I will describe our work on the characteristics and implications of mechanical communication over the ECM.

Global climate models represent small-scale processes, such as clouds and convection, using subgrid models known as parameterizations. Traditional parameterizations are usually based on simplified physical models, and inaccuracies in these parameterizations are a main cause for the large uncertainty in climate projections. One alternative to traditional parameterizations is to use machine learning to learn new parameterizations which are data driven. However, machine-learning parameterizations might violate physical principles and often lead to instabilities when coupled to an atmospheric model. I will show how machine learning algorithms, such as neural networks and random forests, can be used to learn new parameterizations from the output of a three-dimensional high-resolution atmospheric model, while obeying physical constraints such as energy conservation. Implementing these parameterizations in the atmospheric model at coarse resolution leads to stable simulations that replicate the climate of the high-resolution simulation, and capture important statistics such as precipitation extremes. I will also discuss how machine-learning parameterizations can give further insights into the parameterization problem. Specifically, I will show that failures of machine-learning parameterizations can be used to better understand the relationship between large-scale fields and subgrid processes.

The rapid retreat of the Dead Sea during the past decades led the exposure of unique structures of massive gypsum and aragonite crusts: large capes pointing towards the open lake (termed here “gypsum deltas”) and numerous small gypsum mounds scattered on the lake’s exposed shores. Geological field relations, 14C and 34S measurements and thermodynamic calculations provide evidence that the gypsum deltas and the mounds were formed during time-intervals of low lake stands (~420±10 m below mean sea level), when sulfate-rich Ca-chloride brines discharged from the coastal aquifer via saline springs, mixed with the Dead Sea brine and precipitated the gypsum. This mixing process describes a mechanism of “gypsum outsalting”, which is completely different from the conventional view of gypsum as a product of evaporative deposition.
Condition for enhanced saline springs discharge and “gypsum outsalting” occurred in the mid to late Holocene period (~ 6.6 to 0.6 ka), and were mainly intensive at the latest stages of regional aridity cycles when lake level was still low and the Dead Sea salinity was at its highest. The ages of formation of the gypsum structures coincide with times of North Atlantic cooling events and grand solar minima suggesting a direct impact of the latter on the Dead Sea hydrology and high sensitivity of the regional hydrology (controlling lake level) to global solar-related events. The frequency of appearance of the gypsum structures seems to follow the Hallstat Cycle that approached minimum at ~3000 2000 years ago.

Lifestyle dietary interventions are an essential practice in treating obesity, hence neural factors that may assist in predicting individual treatment success are of great significance. Here, in a prospective, open-label, three arms study, we examined the correlation between brain resting-state functional connectivity measured at baseline and weight loss following 6 months of lifestyle intervention in 92 overweight participants. We report a robust subnetwork composed mainly of sensory and motor cortical regions, whose edges correlated with future weight loss. This effect was found regardless of intervention group. Importantly, this main finding was further corroborated using a stringent connectivity-based prediction model assessed with cross-validation thus attesting to its robustness. The engagement of senso-motor regions in this subnetwork is consistent with the over-sensitivity to food cues theory of weight regulation. Finally, we tested an additional hypothesis regarding the role of brain-gastric interaction in this subnetwork, considering recent findings of a cortical network synchronized with gastric activity. Accordingly, we found a significant spatial overlap with the subnetwork reported in the present study. Moreover, power in the gastric basal electric frequency within our reported subnetwork negatively correlated with future weight loss. This finding was specific to the weight loss related subnetwork and to the gastric basal frequency. These findings should be further corroborated by combining direct recordings of gastric activity in future studies. Taken together, these intriguing results may have important implications for our understanding of the etiology of obesity and the mechanism of response to dietary intervention as well as to interoceptive perception.
Zoom link to join:
https://weizmann.zoom.us/j/96608033618?pwd=SEdJUkR2ZzRBZ3laUUdGbWR1VFJTdz09

Meeting ID: 966 0803 3618
Password: 564068

Host: Dr. Rita Schmidt rita.schmidt@weizmann.ac.il tel: 9070