FunSy

The FunSy team combines computational, experimental, and theoretical neuroscience to achieve a seamless integration of neural dynamics research across multiple scales. Our team’s distinctiveness lies in the synergy between electrophysiology, optical recording techniques, and computational modeling, enabling us to investigate neural circuit dynamics in vivo and in silico with novel and synergistic approaches. Our research aims to redefine how functional computations and their dysfunctions in the brain are understood—shifting the focus from circuit “hardware” to the dynamic “software” that orchestrates behavior and cognition. We embrace the hypothesis that dynamic complexity itself is a functional resource, and that temporal fluctuations in neural signals, rather than being noise, are core carriers of information. In this project, we focus on multiscale hippocampal dynamics and their role in memory-guided behavior. By combining high-density silicon probe recordings, fiber photometry across wide networks, and computational modeling, we will dissect how hippocampal spiking assemblies and mesoscopic oscillatory bursts jointly encode behavior, and how this coupling is shaped by learning or disrupted in pathology. Specifically, we will characterize the interplay between firing assemblies and diverse oscillatory patterns—two complex “languages” that the hippocampus appears to speak—and assess how their syntax and mutual interactions reflect and predict behavioral states. Our first aim is to establish predictive correspondences between dictionaries of firing sequences and oscillatory bursts recorded during spatial memory tasks. We hypothesize that these two forms of neural signals do not act independently but are co-organized to convey behavior-related information. Building on this, we propose a novel functional role for oscillatory bursts: they may operate as “attention masks” akin to those in artificial neural networks, dynamically reweighing the influence of past inputs depending on their timing. Preliminary modeling data from our team support this idea, showing how multi-frequency transient bursts shape complex temporal filters modulating spike integration probabilities—thus providing a natural substrate for attention-like computations. Extending beyond the hippocampus, we will characterize how its input network (entorhinal, retrosplenial, prefrontal cortices, and others) contributes additional biasing signals that may dynamically modulate both local spiking and oscillatory activity. Using multifiber photometry, we will track the evolving network-wide state of hippocampal afferents and test the hypothesis that large-scale input network configurations serve as “meta-attentional” states—i.e., higher-order modulators of attention-shaped local circuit processing. Finally, to test the functional relevance of this dynamic complexity, we will probe whether disruptions in hippocampal firing–oscillation interplay are predictive of cognitive impairment in early-stage Alzheimer’s disease models, or conversely, whether environmental enrichment enhances this coupling and improves performance. Our goal is to demonstrate that behavioral competence is supported not just by which neurons are connected, but by how they dynamically coordinate over space and time within a broader system’s dynamics. This highly integrated project bridges experimental and theoretical work across spatial and temporal scales, anchored in cross-disciplinary synergy. It also has methodological implications, from the development of machine-learning-based decoding algorithms to the design of spiking network models implementing emergent attention mechanisms. Ultimately, FunSy’s vision is to uncover principles of brain computation rooted in time-structured, transient coordination—and to translate them into new conceptual frameworks for understanding memory, cognition, and their breakdown in disease.