Events

The Center for Magnetic Resonance Research (CMRR) has been at the forefront of magnetic resonance imaging (MRI) innovation, pioneering ultra-high field (7 Tesla and above) technologies that are revolutionizing brain research and clinical care. This presentation will explore CMRR's groundbreaking journey, from the first functional MRI study to development of high-resolution fMRI capabilities revealing cortical columns within the human cortex. The presentation will also explore the translation of these technologies into clinical practice, with a focus on the unique visualization capabilities of 7T MRI, particularly for enhancing the precision of Deep Brain Stimulation (DBS) procedures.By exploring the progression from the 7T system to the world’s first 10.5T human MRI, this presentation will illustrate how these transformative technologies have pushed the limits of imaging science, uncovering new insights into brain function and advancing personalized clinical care at the intersection of technology, research, and medicine.

The role of neurons in the direction-selective retinal circuit in visual processing in the retina and in the visual thalamus The lateral geniculate nucleus (LGN) of the thalamus is a major retinal target, involved in processing and relaying visual information, including direction selectivity (DS) and orientation selectivity (OS). How DS and OS are organized in the LGN is poorly understood, as well as whether this information is directly inherited from the retina or generated de novo within the LGN. Using extracellular recordings from across the mouse LGN, we studied DS and OS responses and their topographic organization. We found that DS responses are absent in the central visual field, and that their preferred directions are topographically aligned to match translational optic flow patterns in the remaining visual field. OS responses were uniformly distributed throughout the visual field. By eliminating retinal DS in transgenic mice, we found that DS- but not OS-responses in the LGN were dependent on retinal DS. Thus, LGN DS is inherited from the retina, but retinogeniculate transfer may be topography-dependent, optimizing representations that support visually-guided behaviors.

While language models (LMs) show impressive text manipulation capabilities, they also lack commonsense and reasoning abilities and are known to be brittle. In this talk, I will suggest a different LMs design paradigm, inspired by how humans understand it. I will present two papers, both shedding light on human-inspired NLP architectures aimed at delving deeper into the meaning beyond words.  The first paper [1] accounts for the lack of commonsense and reasoning abilities by proposing a paradigm shift in language understanding, drawing inspiration from embodied cognitive linguistics (ECL). In this position paper we propose a new architecture that treats language as inherently executable, grounded in embodied interaction, and driven by metaphoric reasoning.  The second paper [2] shows that LMs are brittle and far from human performance in their concept-understanding and abstraction capabilities. We argue this is due to their token-based objectives, and implement a concept-aware post-processing manipulation, showing it matches human intuition better. We then pave the way for more concept-aware training paradigms.   [1] Language (Re)modelling: Towards Embodied Language Understanding Ronen Tamari, Chen Shani, Tom Hope, Miriam R L Petruck, Omri Abend, and Dafna Shahaf. 2020. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 6268–6281, Online. Association for Computational Linguistics.  [2] Towards Concept-Aware Large Language Models Shani, Chen, Jilles Vreeken, and Dafna Shahaf. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 13158-13170. 2023.   Bio: Chen Shani is a post-doctoral researcher at Stanford's NLP group, collaborating with Prof. Dan Jurafsky. Previously, she pursued her Ph.D. at the Hebrew University under the guidance of Prof. Dafna Shahaf and worked at Amazon Research. Her focus lies at the intersection of humans and NLP, where she implements insights from human cognition to improve NLP systems.

While cognitive neuroscientists have uncovered principles of perceptual decision-making by analyzing choices and neuronal firing across thousands of trials, we do not yet know the behavioral or neuronal dynamics underlying one SINGLE choice. For instance, why might a subject judge a given stimulus in category A 70% of the time but in category B 30%? Until we can work out precisely what determines single-decisions – this choice, right now – the mechanisms of real-world decision-making will remain unknown. In tactile psychophysical tasks with rats and humans, we are trying to sort out factors that explain the variability in judgments (across trials) to the identical stimulus input. We identify four factors: (i) trial-to-trial fluctuations in sensory coding, (ii) temporal context, namely, the history of preceding stimuli and choices, (iii) attention, and (iv) bias (predictions originating in beliefs about the environment’s probabilistic structure). The strategy is to bring these factors under experimental control, rather than leaving them to vary according to uninterrogated states within the subject. Psychophysics from rats and humans show that large chunks of variability are accounted for by these factors; evidence from cortical neuronal populations in rats provides some mechanistic grounding.

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.

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.

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

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.

For centuries, consciousness was considered to be outside the reach of scientific investigation. Yet in recent decades, more and more studies have tried to probe the neural correlates of conscious experience, and several neuronally-inspired theories for consciousness have emerged. In this talk, I will focus on four leading theories of consciousness: Global Neuronal Workspace (GNW), integrated Information Theory (IIT), Recurrent Processing Theory (RPT) and Higher Order Theory (HOT). I will first shortly present the guiding principles of these theories. Then, I will provide a bird's-eye view of the field, using the results of a large-scale quantitative and analytic review we conducted, examining all studies that either empirically tested these theories or interpreted their findings with respect to at least one of them. Finally, I will describe the first results of the Cogitate consortium - an adversarial collaboration aimed at testing GNW and IIT.

Sensory processing is fundamental for animal adaptation and survival, linking them to their environments. Understanding the nervous system's integration of sensory information is crucial for comprehending behavior and cognition. This process involves integrating external cues across modalities, along with internal states, cognitive processes, and motor control, leading to complex behaviors and a nuanced understanding of the world. To facilitate research on these processes, we aimed to identify natural behaviors that produce both auditory and somatosensory stimuli, steering clear of artificial stimulus sources. We discovered that whisking, previously considered a unimodal behavior associated solely with tactile sensations, also produces sounds with distinctive acoustic features within the auditory frequency range of mice. We explored the auditory neuronal representation of sounds generated by whisking and their implications for behavioral performance.  We demonstrate that sounds produced by whisking elicit diverse neuronal responses in the auditory cortex, encoding the object's identity and the mouse's whisking state, even in the absence of tactile sensations. Furthermore, we show that mice are capable of completing behavioral tasks relying solely on auditory cues generated by whisking against objects.

How information is processed within the brain is a key question in systems neuroscience. We address the issue in spiking neuronal networks of freely moving mice. I will describe our recent findings and conclusions pertaining to three specific information processing steps: transmission, representation, and storage. First, using feedforward optogenetic injection of white noise input to a small group of adjacent neocortical excitatory cells, we find that spike transmission to a postsynaptic cell exhibits error correction, improved precision, and temporal coding. The results are consistent with a nonlinear coincidence detection model in the postsynaptic neuron. Second, by triggering input on animal kinematics, we create an artificial place field in an otherwise-silent pyramidal cell. In hippocampal region CA1 but not in the neocortex, artificial fields exhibit synthetic phase precession that persists for a full cycle. The local conversion of an induced rate code into an emerging phase code is compatible with a dual-oscillator interference model. Third, by triggering input on spontaneous spiking, we impose self-terminating spike patterns in a group of presynaptic excitatory neurons and a postsynaptic cell. The precise timing of all pre- and postsynaptic spikes has a more substantial impact on long-lasting effective connectivity than that of individual cell pairs, revealing an unexpected plasticity mechanism. We conclude that intrinsic properties of single neurons support millisecond-timescale operations, and that cortical networks are organized in functional modules which we refer to as “converging assemblies”.

Aging and neurodegeneration are associated with changes in brain tissue at the molecular level, affecting its organization, density, and composition. These changes can be detected using quantitative MRI (qMRI), which provides physical measures that are sensitive to structural alterations. However, a major challenge in brain research is to relate physical estimates to their underlying biological sources. In this talk, I will discuss the community's efforts to use qMRI to identify biological processes that underlie changes in brain tissue. Specifically, I will highlight approaches for differentiating between changes in the concentration and composition of myelin and iron during aging. By exploring the molecular landscape of the aging and neurodegenerative brain using qMRI, we aim to gain a better understanding of these processes and potentially provide new metrics for evaluating them.