Past events

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

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

Formation and retrieval of distinct memories are complementary processes that put conflicting requirements on neuronal computations in the hippocampus, especially when memories closely resemble each other. How this challenge is resolved in hippocampal circuits to guide memory-based decisions is unclear. To address this question, our group uses in vivo 2-photon calcium imaging and whole-cell recordings from hippocampal subregions in head-fixed mice trained to distinguish between novel and familiar virtual-reality environments. We find that granule cells consistently show a small transient depolarization of their membrane potential upon transition to a novel environment. This synaptic novelty signal is sensitive to local application of atropine, indicating that it depends on metabotropic acetylcholine receptors. A computational model suggests that the observed transient synaptic response to novel environments leads to a bias in the granule cell population activity, which can in turn drive the downstream attractor networks to a new state, thereby favoring the switch from generalization to discrimination when faced with novelty. Such a novelty-driven cholinergic switch may enable flexible encoding of new memories while preserving stable retrieval of familiar ones. 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

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