Senior lecturer
Our lab operates at the intersection of machine learning and systems neuroscience, focusing on developing analytical and conceptual frameworks for inferring the function of brain circuits from large-scale neuronal and behavioral data. Leveraging the current unprecedented capabilities to monitor and manipulate the activity of many neurons, novel computational tools hold the promise of enhancing our capacity to characterize and interpret complex neuronal dynamics in relation to cognition, action, and sensation. In synergy with our close experimental collaborators, we specifically develop analytical approaches to extract structures in high dimensional neuronal dynamics to gain insights regarding distributed neural code of adaptive behavior, and its evolution both in healthy conditions (e.g. learning) and in maladaptive and pathological conditions (e.g. compulsion, addiction).
Fustiñana MS, Eichlisberger T, Bouwmeester T, Bitterman Y, Lüthi A. State-dependent encoding of exploratory behaviour in the amygdala. Nature. 2021 Apr;592(7853):267-271. doi: 10.1038/s41586-021-03301-z.
The behaviour of an animal is determined by metabolic, emotional and social factors1,2. Depending on its state, an animal will focus on avoiding threats, foraging for food or on social interactions, and will display the appropriate behavioural repertoire3. Moreover, survival and reproduction depend on the ability of an animal to adapt to changes in the environment by prioritizing the appropriate state4. Although these states are thought to be associated with particular functional configurations of large-brain systems5,6, the underlying principles are poorly understood. Here we use deep-brain calcium imaging of mice engaged in spatial or social exploration to investigate how these processes are represented at the neuronal population level in the basolateral amygdala, which is a region of the brain that integrates emotional, social and metabolic information. We demonstrate that the basolateral amygdala encodes engagement in exploratory behaviour by means of two large, functionally anticorrelated ensembles that exhibit slow dynamics. We found that spatial and social exploration were encoded by orthogonal pairs of ensembles with stable and hierarchical allocation of neurons according to the saliency of the stimulus. These findings reveal that the basolateral amygdala acts as a low-dimensional, but context-dependent, hierarchical classifier that encodes state-dependent behavioural repertoires. This computational function may have a fundamental role in the regulation of internal states in health and disease.
We are looking for highly motivated postdoc and student to join the lab.
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My scientific journey began with studying Physics and the Humanities (Amirim) at the Hebrew University. I then joined the ICNC (Interdisciplinary Center for Neural Computational, now ELSC), where I studied complex auditory representations in the auditory cortex of mice and humans in the lab of Prof. Israel Nelken at the Hebrew University. It was there that I experienced firsthand the power of a research program integrating independent analytical framework development with domain-specific experimental
data, to drive new discoveries and conceptual advancement.
For my postdoc, I joined the lab of Prof. Andreas Luthi, at the Friedrich Miescher Institute in Basel, Switzerland, as it was establishing imaging of large populations of identified neurons from deep brain structures of freely behaving mice over weeks. Harnessing the size and scope of the new datasets, I developed a new approach to extract dynamical structure from the high dimensional activity of the local network and applied it to study amygdala’s function in free social interaction, spatial exploration and
motivational control of behavior.
In 2023 I joined the Faculty of Medicine and opened a computational lab in the Department of Medical Neurobiology. The core focus of the lab’s research lies at the intersection of machine learning and systems neuroscience, with an emphasis on developing novel analytical approaches to describe the
dynamics of large neuronal population activity. Our goal is to work in synergy with our experimental collaborators to link meso-scale brain network dynamics with adaptive (and maladaptive, compulsive/addictive) behavior in order to provide insights regarding neuronal network function as well as novel data-driven analytical approaches with general applicability.
