During sleep, our memories are reactivated and consolidated in an active process that significantly influences our memory and decision-making. In this talk, I will present two studies about sleep-memory consolidation. The first study investigated sleep memory consolidation's local versus global properties within the brain. By exploiting the unique functional neuroanatomy of olfactory system, we were able to manipulate sleep oscillations and enhance memories locally within a single hemisphere during sleep. These findings underscore the local nature of sleep memory consolidation, which can be selectively manipulated within the brain, thereby creating an important link between theories of local sleep and learning. The second research explored the relationship between generalization processes and sleep, acknowledging that overgeneralization of negative stimuli and disruptions in sleep quality contribute to anxiety and PTSD disorders. Specifically, we studied participants' responses to stimuli associated with positive, negative, or neutral outcomes. Our findings revealed significant correlations between brain activity, as detected by fMRI, during the association of a stimulus with an outcome and the perceptual generalization of these stimuli. While activity in limbic brain areas was correlated with immediate negative stimulus generalization, we observed that the activation in these areas predicted recovery and positively related generalization following sleep. Moreover, we identified specific sleep oscillations correlated with this recovery generalization using high-density EEG recordings. These results highlight the crucial role of sleep in both generalization processes and the restoration of balanced responses to stimuli. Understanding these mechanisms can offer valuable insights into developing therapeutic strategies for anxiety and PTSD.

The capacity of animals to respond to stimuli in their surroundings is crucial for their survival. In mammals, complex evaluations of the environment require large numbers and different subtypes of neurons. The nematode C. elegans utilize its compact nervous system to process environmental cues and tune behavior. Integration of opposing spatial information and adaptation to distinct types of addictive substances are only a few challenges that require efficient and effective use of the worm’s compact nervous system. We describe how distinct environmental cues can converge onto common neural networks and molecular mechanisms but generate diverse neuronal and behavioral responses. Using a multidisciplinary approach, we completed several parallel aims, including the development of two novel research methods

The study of human memory is a rich field with a history that spans over a century, traditionally investigated through the prism of psychology. Drawing inspiration from this vast pool of findings, we approached the subject with a more physics-oriented mindset based on first principles. For this reason, we combined mathematical modelling of established ideas from the literature of psychology with large-scale experimentation. In particular, we created a model based on the concept of retroactive interference that states that newly encoded items hinder the retention of older ones in memory. We show that this simple mechanism is sufficient to describe a variety of experimental data of recognition memory with different categories of verbal and pictorial stimuli. The model has a single free parameter and can be solved analytically. We then focus on recall and recognition memory of stories. This transition from discrete random lists to coherent continuous stimuli such as stories introduces a new challenge when it comes to the quantification and the analysis of the results. To address this, we have developed a pipeline that employs large language models and showed that it performs comparably to human evaluators. Using this tool we were able to show that recall scales linearly with recognition and story size for the range we examined. Finally, we discovered that when stories are presented in a scrambled manner, even though recall performance drops, subjects seem to reconstruct the material in their recall in alignment to the unscrambled version.

1.  Can we reconstruct images that a person saw, directly from his/her fMRI brain recordings?  2.  Can we reconstruct the training data that a deep-network trained on, directly from the parameters of the network?   The answer to both of these intriguing questions is “Yes!”  In this talk I will show how these can be done. I will then show how exploring the two domains in tandem can potentially lead to significant breakthroughs in both fields. More specifically: (i)  I will show how combining the power of Brains & Machines can potentially be used to bridge the gap between those two domains. (ii) Combining the power of Multiple Brains (scanned on different fMRI scanners with NO shared stimuli) can lead to new breakthroughs and discoveries in Brain-Science. We refer to this as “the Wisdom of a Crowd of Brains”. In particular, we show that a Universal Encoder can be trained on multiple brains with no shared data,  and that information can be functionally mapped between different brains.