I was born in Argentina, moved to the US when I was 8. I did my undergrad at Duke University in Biology and Philosophy and my PhD at Rutgers University in Neuroscience.  Under the mentorship of Dr. Gyorgy Buzsáki, I focused on relating the ‘content’ of memory to the ‘context’ of the brain’s internally generated rhythms. First, I described a new homeostatic role for REM sleep in homogenizing firing rates across neural populations during sleep (Grosmark et al., 2012). In subsequent work, I studied the memory content of hippocampal sharp-wave/ripples (SWR), synchronous population activity events that are biomarkers for memory processing. Challenging the dominant theory in the field, I described that SWR-related memory replay of novel experiences is the joint product of two specific place cell sub-networks: a sub-network of low-firing rate ‘plastic’ neurons reflecting learned content, and a ‘rigid’ sub-network of high-firing rate neurons reflecting unchanging internal dynamics (Grosmark & Buzsáki, 2016). Moreover, in keeping with my dedication to open-science and collaboration , the dataset for this study has been made publicly available and has subsequently been used in more than 18 published articles and book chapters.

For my post-doctoral training, I joined Dr. Attila Losonczy’s lab at Columbia University where I combined my knowledge of large-scale silicon probe recordings with training in hippocampal two-photon imaging. Utilizing these two techniques simultaneously, I performed the first experiments that tracked the formation, consolidation and re-expression of distinct hippocampal spatial memory traces across behavioral states from the short time-scales of SWRs (tens-of-milliseconds) through their multi-week evolution (Grosmark et al., 2021). I describe that memory-specific reactivation is long-lasting, and that recruitment of place cells to offline reactivation events predicts their long-term representational stability days in advance of online memory recall. Notably, I showed that rather than uniformly consolidating all representations, post-learning reactivation uniquely stabilizes the portions of the cognitive map that are most vulnerable to being forgotten. This suggests that post-learning consolidation plays a computationally distinct and complementary role in learning compared to online encoding. I will continue to explore the function and dysfunction of this fascinating division of cognitive labor.

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