Integrated Calendar

Decision making is a fundamental ability to human life. Even the simplest decision we make requires integration of multiple factors in our brain, such as prior knowledge, information from the environment, emotions and many more. Despite many years of research and numerous important and ground-breaking findings on how learning and decision-making are generated in our brain, a lot of knowledge is still required for a comprehensive understanding of it. My research initiated from the motivation to understand the unique contribution of valence (rewards and punishments) – when presented as feedback during learning – to perception and decision-making. For that purpose, I studied multiple groups of individuals under different experimental conditions created to elucidate behavioral and neural responses to rewards and punishments. I asked how prediction errors (PE, the difference between expected and received outcomes) bias the perception of time, and how valence and information from feedback, factors that are often indistinguishable, differently guide decision making in a multi-choice environment. Using functional MRI and computational models, I found that positive and negative PEs, known to drive learning, bias the perception of time in opposite directions. Positive PEs induce change in the perceived time so it seems longer compared to a neutral condition (no PE). In contrast, when a negative PE is detected, time is perceived to be shorter. My results identify the Putamen, a structure that receives dopaminergic projections and is involved in time perception, as the brain region that likely drives this bias and underlies the interaction between time perception and prediction-errors.
In addition, I demonstrated that knowing the outcome valence in advance can enable an information-based decision making, namely one that is not affected by the valence itself and is driven only by the information available in the environment. Because uncertainty regarding choice increases when more options are available to choose from, a ‘right’ feedback provides more information to the learning process, compared to a ‘wrong’ feedback. This was accompanied by a differential activation in the ACC, PFC and striatum. Importantly, in this context, punishment avoidance is equally rewarding, and indeed I found that choice behavior and the neural networks underlying choice and feedback processing are similar in the two scenarios – for punishments and rewards. Overall, my work develops and suggests computational and neural mechanisms for specific roles of the information carried by prediction-errors. These findings can enhance our understanding of the fundamental roles of valence and information gain during learning and decision making.

Zoom link to join: https://weizmann.zoom.us/j/92234357805?pwd=aVkrR21CSUVtVS9tSEJYRDkwOFRidz09

Meeting ID: 922 3435 7805
Password: 648092

We often study plasticity of highly synthetic environments that may not necessarily form the substrate of more realistic conditions. We study sensory systems using both synthetic and more natural forms of plasticity in hope to find common brain mechanisms. On one hand we study perceptual and category learning and on the other hand parental plasticity; both in the auditory and olfactory systems. Using mice we exploit the available experimental toolkit to reveal anatomical, physiological and behavioral manifestation of plasticity in both synthetic and more natural conditions. I will discuss our efforts to study auditory plasticity in the context of mother-infant bonding, an interaction that rapidly develops following parturition. Specifically, I will describe how pup vocalizations are represented in the brain of naïve mice and in mothers, when they first start caring for their newborn pups. I will also share our recent efforts to study perceptual and category learning of synthetic (both simple and complex) environments.


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

New protein interaction specificities are created in evolution through genetic duplication and mutation of interacting pairs. Due to the stringent molecular constraints on mutations in protein-interaction surfaces and the astronomical number of possible mutational trajectories that may lead from one natural pair to another, no plausible path that connects two interacting pairs has been described. I developed a general strategy to compute minimally frustrated mutational trajectories that connect any two extant pairs. I then demonstrated that a computed path for a bacterial toxin-antitoxin pair is evolutionarily plausible with 18 intermediate mutant pairs that are fully functional in vivo. My results reveal a previously unknown mechanism by which the stringency of the specificity-switch point on the path may be relaxed by mutations that are not observed in the evolutionary record.