Functional ultrasound imaging (fUS) is an emerging neuroimaging tool capable of measuring brain-wide vascular signals linked to neuronal activity with a high spatial-temporal resolution (100 µm, 10 Hz) in real-time. This technology is portable, affordable and adaptable to many species, and has already found applications in areas ranging from basic research to the clinic. Focusing on fundamental neuroscience, I will outline some of the recent technical advancements of fUS, such as the capacity to image the entire rodent brain while manipulating specific neuronal circuits with optogenetics. I will exemplify how promising this imaging technique is for shedding new light on the brain-wide circuits underlying behavior, as fUS is one of the few methods that enables imaging of activity deep in the brain of behaving mice. Zoom link: https://weizmann.zoom.us/j/95406893197?pwd=REt5L1g3SmprMUhrK3dpUDJVeHlrZz09 Meeting ID: 954 0689 3197 Password: 750421

Maintaining average activity level within a set-point range constitutes a fundamental property of central neural circuits. Accumulated evidence suggests that firing rate distributions and their means represent physiological variables regulated by homeostatic systems. Utilizing basic concepts of control theory, we developed a theoretical and experimental framework for identifying the core members of homeostatic machinery. I will describe an integrative approach to study the relationships between ongoing spiking activity of individual neurons and neuronal populations in local microcircuits, synaptic transmission and plasticity, sleep and memory functions. I will show our new data on a state-dependent regulation of firing rate set-points, their dysregulation at the presymptomatic stage of Alzheimer’s disease, and the role of mitochondria in these processes.

In the first part of my talk I will show how complex visual inference tasks can be performed with Deep-Learning, in a totally unsupervised way, by training on a single image -- the test image alone. The strong recurrence of information inside a single natural image provides powerful internal examples which suffice for self-supervision of Deep-Networks, without any prior examples or training data. This new paradigm gives rise to true “Zero-Shot Learning”. I will show the power of this approach to a variety of visual tasks, including super-resolution, image-segmentation, transparent layer separation, image-dehazing, and more. In the second part of my talk I will show how self-supervision can be used for “Mind-Reading” (recovering observed visual information from fMRI brain recordings), when only very few fMRI training examples are available.

Social interactions are essential for animals to survive, reproduce, raise their young. Over the years, my lab has attempted to decipher the unique characteristics of social recognition: what are the unique cues that trigger distinct social behaviors, what is the nature and identity of social behavior circuits, how is the function of these circuits different in males and females and how are they modulated by the animal physiological status? In this lecture, I will describe our recent progress in understanding how different parts of the brain participate in the positive and negative control of parental behavior in males and females, providing a new framework to understand the regulation of adult-infant interactions in health and disease. I will also describe how new approaches in in situ single cell transcript omics have enabled us to uncover specific hypothalamic cell populations involved in distinct social behaviors. Finally, I will describe our most recent work uncovering how specific brain circuits are able to direct adaptive changes in behavior during sickness episodes in mice. Host: Dr. Takashi Kawashima takashi.kawashima@weizmann.ac.il tel: 2995