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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/ 

While AI has achieved remarkable advancements in areas such as image recognition and natural language processing, its application in Earth and environmental sciences is still emerging. Unprecedented data from satellites, remote sensors, and in-situ measurements offers new opportunities to improve physics-based model forecasts of environmental systems with AI and to gain deeper insights. However, extreme systems as weather and climate events, pose distinct challenges for AI, such as limited sampling of rare events, non-trivial data augmentation, errors-in-variables, and complexities of transfer learning across diverse tasks. In this talk, I will explore these challenges and showcase AI architectures designed to address them. I will use specific examples of forecasting dust storms, precipitation extremes, and drought events in the Middle East.

Structured State Space Models (SSMs) are emerging as efficient alternatives to Transformers, forming the backbone of neural architectures such as S4 and Mamba. In this talk, I will present a series of works theoretically analyzing SSMs. I will begin with the implicit bias of Gradient Descent (GD) over SSMs, proving that it often leads to generalization, but is susceptible to clean-label poisoning attacks. I will then tackle the open question of the benefits of complex parameterizations for SSMs, proving formal separations between real and complex parameterizations: a real SSM can only match a complex SSM if either the dimension of the real SSM or the number of iterations required for its training is exponentially large. Taken together, the presented findings deepen our theoretical understanding of SSMs, and highlight their potential towards interpretable state-of-the-art AI systems.

Covered works were in collaboration with Yotam Alexander, Avichai Ben David, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Eden Lumbroso, Itamar Menuhin-Gruman, Yuval Ran-Milo, Noam Razin and Yonatan Slutzky.

Despite covering over a third of Earth’s land surface, arid regions remain among the least understood hydrological environments. Practically every component of the desert water cycle is more poorly constrained than its counterpart in wetter regions. Yet deserts are home to over 20% of the global population and are disproportionately vulnerable to hydrometeorological hazards such as droughts, floods, and the accelerating impacts of climate change. A better understanding of the desert water cycle is therefore not only a scientific challenge, but a critical need for sustainable water resource and risk management in drylands.In this talk, I will present three studies that illuminate different aspects of the desert water cycle:(a)  how satellite observations can be used to infer the (underwater) topography — and thus the water volume — of remote desert lakes;(b) what atmospheric ingredients link moisture, rain, and floods in the hyperarid Sahara, and how these relate to the desert's paleo- (and future?) climate; and(c)  how misjudged flood risk management on the desert margin contributed to the deadliest hydrometeorological disaster of the 21st century in Derna, Libya.Together, these studies illustrate how unconventional combinations of satellite data and modelling can overcome the challenges of limited in situ observations to reconstruct, quantify, and ultimately understand hydrological processes in deserts. They also challenge longstanding assumptions about runoff generation and risk mitigation in arid regions, pushing the boundaries of what we thought we could know in some of the world's most water-scarce landscapes.