Integrated Calendar

Contamination of drinking water sources by a variety of organic and inorganic compounds demands more efficacious and reliable treatment technologies. However, conventional water treatment technologies remain chemically demanding, energy intensive, and ineffective in removing key trace contaminants. As such, nanotechnology-based approaches have been increasingly explored to enhance or replace traditional remediation methods because of the high reactivity and tunable-properties of nanomaterials. In her talk, Dr. Zucker will provide an overview on the current status of nano-enabled water decontamination, including promising opportunities and barriers for implementation. Specifically, the application of molybdenum disulfide (MoS2) for heavy metal removal will be extensively discussed as a case study, where material properties, removal mechanisms, and large-scale applications are optimized.

The hippocampus and related medial temporal lobe structures (entorhinal cortex, perirhinal cortex, etc.) play a vital role in transforming experience into long-term memories that are then stored in the cortex, however the cellular mechanisms which designate single neurons to be part of a memory trace remain unknown. Part of the difficulty in addressing the mechanisms of transformation of short-term to long-term memories is the distributed nature of the resulting “engram” at synapses throughout the cortex. We therefore used a behavioral paradigm dependent on both the hippocampus and neocortex that enabled us to generate memory traces rapidly and reliably in a specific cortical location, by training rodents to associate the direct electrical microstimulation of the primary sensory neocortex with a reward. We found that medial-temporal input to neocortical Layer 1 (L1) gated the emergence of specific firing responses in subpopulations of Layer 5 pyramidal neurons marked by increased burstiness related to apical dendritic activity. Following learning and during memory retrieval, these neocortical responses became independent of the medial-temporal influence but continued to evoke behaviour with single bursts sufficient to elicit a correct response. These findings suggest that L1 is the locus for hippocampal-dependent associative learning in the neocortex, where memory engrams are established in subsets of pyramidal neurons by enhancing the sensitivity of tuft dendrites to contextual inputs and driving burst firing.

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

Computational problems may be solved by realizing physics systems that can simulate them. Here we present a new system of coupled lasers in a modified degenerate cavity that is used to solve difficult computational tasks. The degenerate cavity possesses a huge number of degrees of freedom (300,000 modes in our system), that can be coupled and controlled with direct access to both the x-space and k-space components of the lasing mode. Placing constraints on these components are mapped on different computational minimization problems. Due to mode competition, the lasers select the mode with minimal loss to find the solution. We demonstrate this ability for simulating XY spin systems and finding their ground state, for phase retrieval, for imaging through scattering medium, and more.

We all capture the world around us through digital data such as images, videos and sound. However, in many cases, we are interested in certain properties of the data that are either not available or difficult to perceive directly from the input signal. My goal is to “Re-render Reality”, i.e., develop algorithms that analyze digital signals and then create a new version of it that allows us to see and hear better. In this talk, I’ll present a variety of methodologies aimed at enhancing the way we perceive our world through modified, re-rendered output. These works combine ideas from signal processing, optimization, computer graphics, and machine learning, and address a wide range of applications. More specifically, I’ll demonstrate how we can automatically reveal subtle geometric imperfection in images, visualize human motion in 3D, and use visual signals to help us separate and mute interference sound in a video.

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

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.

Zoom link to join: https://weizmann.zoom.us/j/93360836031?pwd=dDZEdTQ1QUkxUVVONVErVm9CcUJWQT09



Meeting ID: 933 6083 6031
Password: 591230

https://drive.google.com/file/d/1KqUjdjfrPYXDCNa3_0Z83i-vBxl-dgrL/view?usp=sharing