I am currently a PhD candidate in the Biological Sciences Program at UCSD, where I am fortunate to work with Prof. Marcus Benna. I received my MSc in Systems Biology at Maastricht University. I completed my master thesis on computational neuroscience working in the lab of Stefano Fusi at Columbia University.
My research is on how populations of neurons encode information within ethological contexts. Specifically, I am interested in how changes within an animal's internal state lead to changes within its neural representations and how these representations evolve over time. For instance, how chronic mild stress can induce depression.
The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits.
The primary motor cortex (M1) is central for the learning and execution of dexterous motor skills1-3, and its superficial layer (layers 2 and 3; hereafter, L2/3) is a key locus of learning-related plasticity1,4-6. It remains unknown how motor learning shapes the way in which upstream regions activate M1 circuits to execute learned movements. Here, using longitudinal axonal imaging of the main inputs to M1 L2/3 in mice, we show that the motor thalamus is the key input source that encodes learned movements in experts (animals trained for two weeks). We then use optogenetics to identify the subset of M1 L2/3 neurons that are strongly driven by thalamic inputs before and after learning. We find that the thalamic influence on M1 changes with learning, such that the motor thalamus preferentially activates the M1 neurons that encode learned movements in experts. Inactivation of the thalamic inputs to M1 in experts impairs learned movements. Our study shows that motor learning reshapes the thalamic influence on M1 to enable the reliable execution of learned movements.
Read more →Here, we present the amygdalostriatal transition zone (ASt) as a missing piece of a highly conserved process of paramount importance for survival, which represents an internal state (e.g. fear) that can be expressed in multiple motor outputs (e.g. freezing or escape). From in vivo cellular resolution recordings that include both electrophysiology and calcium imaging, we find that ASt neurons are sparse coding, extremely high signal-to-noise, and maintain a sustained response for negative valence stimuli for the duration of the defensive behavior.
Read more →How do social factors impact the brain and contribute to increased alcohol drinking? We found that social rank predicts alcohol drinking, where subordinates drink more than dominants. Furthermore, social isolation escalates alcohol drinking, particularly impacting subordinates who display a greater increase in alcohol drinking compared to dominants.
Read more →Most social species self-organize into dominance hierarchies, which decreases aggression and conserves energy, but it is not clear how individuals know their social rank. We have only begun to learn how the brain represents social rank, and guides behavior on the basis of this representation. The medial prefrontal cortex (mPFC) is involved in social dominance in rodents, and humans. Yet, precisely how the mPFC encodes relative social rank and which circuits mediate this computation is not known.
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