לוח שנה משולב

I will explain the construction of Ramanujan graphs based on the “method of interlacing families” introduced by Adam Marcus, Daniel Spielman and Nikhil Srivastava. This method was also used to prove two theorems known to imply a positive solution to the celebrated Kadison–Singer problem.

In this talk, I will present recent studies in our lab, where new tools are pushing the boundaries of sleep research.In a first line of studies, we are investigating how sleep promotes memory consolidation in humans. To this end, we developed a novel ecological paradigm to study episodic memory without report; upon repeated viewing of special movies ,eye gaze patterns can quantify memory for specific events. Next, we show that deep brain closed-loop intracranial electrical stimulation during human sleep enhances hippocampus-cortex synchrony and memory performance. Finally, we examine how sleep and memory are disrupted in amnestic mild cognitive impairment (aMCI) representing early Alzheimer’s disease (AD). To this end, we developed a novelmachine learning-based method to non-invasively detect interictal spikes occurring in the medial temporal lobe during sleep. This approach can help identify disruptions in hippocampus-cortex synchrony and memory consolidation during sleep.In the second line of studies, we investigate how reduced locus coeruleus-noradrenaline (LC-NE) activity during sleep mediates sensory disconnection. In rodents, the level of ongoing tonic LC activity during sleep anticipates sound-evoked awakenings, while minimal optogenetic LC activation or silencing increases and decreases such awakenings, respectively. Projection-specific investigation in mice indicates that an early surge of brainstem NE is particularly important for mediating sensory-evoked awakenings. These findings may shift how we view arousal- promoting neuromodulation; rather than acting in a diffuse hormonal-like manner, the LC-NE system may in fact operate through specific projection pathways, in a circuit-like manner. Finally, I will describe a novel method for monitoring gaze direction and pupil size through closed eyes in humans via short-wave infrared (SWIR) imaging in combination with video analysis algorithms. This technology has potential to enable touchless and continuous monitoring of depth of anesthesia, pain, and detection of intraoperative awareness.

In recent years, the landscape of artificial intelligence (AI) has been reshaped by the rapid emergence of Foundation Models (FMs). These versatile models have garnered widespread attention for their remarkable ability to transcend the boundaries of traditional, bespoke AI solutions and to generalize to a large set of downstream tasks.   In this presentation we will describe the development of geospatial FMs with earth observation and weather data and discuss the initial results of such models when fine-tuned to various applications including flood detection, CO2 monitoring, nature-based carbon sequestration. We will also show how such foundation models can be a new and exciting tool for assisting and accelerating scientific discovery.

Since the groundbreaking discovery of 51 Pegasi b in 1995, a few thousands extra-solar planets have been identified, marking the beginning of a rapidly evolving new field in Astronomy. This talk will trace the history of exoplanet research and explore how these discoveries have reshaped our understanding of planetary formation and opening new frontiers in the study of planetary systems.

Visual recognition of materials and their states is essential for understanding the world, from determining whether food is cooked, metal is rusted, or a chemical reaction has occurred. Collecting data that captures this vast variability is complex due to the scattered and gradual nature of material states. Manually annotating real-world images is constrained by cost and precision, while synthetic data, although accurate and inexpensive, lacks real-world diversity. This work aims to bridge this gap by infusing patterns automatically extracted from real-world images into synthetic data. We show that neural nets trained on this data outperform state-of-the-art zero-shot models like Clip and SAM on material recognition and segmentation tasks.

Reference:
Drehwald, Manuel S., et al. "One-shot recognition of any material anywhere using contrastive learning with physics-based rendering." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.
‏Eppel, Sagi, et al. "Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation." The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track. 2024

Bio:
Researcher at the Weizmann Institute AI Hub with a Ph.D. in Chemistry and a postdoc in Materials Engineering. Research topics include:  solar cells,  crystallization,  computer vision, self-driving cars, and autonomous laboratories in academia and industry.

The map of synaptic connectivity between neurons shapes the computations that neural circuits carry - making the identification of the design principles of neural “connectomes” crucial for understanding brain development, learning, information processing, and behavior.We present a class of probabilistic generative models for the connectomes of different brain areas in zebrafish, worm, and mouse. Our models accurately replicate a wide range of circuit properties - synapse existence and strength, neuronal in-degree and out-degree, and sub-network motif frequencies -  using surprisingly small sets of biological and physical architectural features. We then show that simulated synthetic circuits generated by our models recapitulate the neural activity and computation performed by the real ones. We extend these generative models to study the development of connectomes over time, and show they accurately replicate the “developmental trajectory” of the connectome of C. elegans, revealing a simpler set of functional cell types than commonly assumed, and identifying distinct developmental epochs. We further study structure-function relationships in simulated spiking neural networks and learn a metric that predicts the similarity of networks based on a small set of architectural features. Our findings suggest that connectomes across species follow surprisingly simple design principles and offer a general computational framework for analyzing connectomes, linking their structure to function. FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/