Schulze Lab

Rich Behavior, Flexibility, and Individuality in Social Context

The Schulze Lab addresses a central question: How do neural circuits compute the experience-dependent actions animals deploy in complex worlds?

Much of animal life unfolds in groups, where individuals balance self-interest with social information, deciding whether to compete or cooperate, follow or lead, stay or flee. These decisions depend on experience, internal state, and context, and are shaped by individual traits such as boldness, caution, sociability, or aggression.

However, it remains largely unknown how neural circuits generate flexible behaviors, and how individuality and group structure interact to shape both collective outcomes and personal trajectories. 

We aim to connect circuit dynamics to adaptive behavior by uncovering how the brain integrates sensory input, internal state, background, and learning to guide decision-making, going beyond the constrained, binary choices of traditional laboratory paradigms, toward the rich, ambiguous decisions animals face in natural social environments. Further, we  investigate how vertebrate brains maintain individuality while synchronizing with others, a fundamental challenge of social life and emergence.

Multiscale Approaches: From Natural Behavior to Circuit Mechanism

To answer questions around how individuality and group dynamics shape decisions across the brain, the SchulzeLab works with Danionella cerebrum, a small, behaviorally rich, transparent vertebrate whose adult nervous system remains optically accessible. 

Lifelong transparency and tiny size make it possible to monitor activity across the entire brain during complex behaviors, capturing how social decisions emerge from distributed network dynamics. We use this genetically amenable teleost as a window into the social brain and bridge natural behavior and neural computation by combining freely moving paradigms, posture tracking, and machine-learning–based behavioral profiling with whole-brain calcium imaging at single-cell resolution.

To dissect the underlying mechanisms with precision, we build near-naturalistic closed-loop virtual reality systems, where head-fixed fish interact with virtual conspecifics or objects under realistic feedback. These systems preserve ecological richness while allowing us to manipulate sensory and social cues systematically. By alternating between freely swimming assays (to discover behavioral rules) and virtual reality experiments (to test underlying neural computations), we connect real-world flexibility to its circuit-level implementation. Finally, we combine functional imaging with molecular mapping. Using multiplexed EASi-FISH, we identify the circuits and cell types underlying individuality, social decision-making and stress.
Computational analyses, including dimensionality reduction, predictive modeling, and information-theoretic approaches, reveal how neural populations encode behavioral variables, cognitive processes, and social context, and how these codes evolve with development and experience.