Emily Mackevicius, Fee Lab

Title: Learning the simple building blocks of a complex motor program

Abstract: How does the brain learn complex behaviors? Does it break them into simpler pieces? How are goals represented, and translated into actions that produce those goals? Songbirds learn to imitate the songs of tutors they heard as juveniles. From unstructured babbling, repeatable syllables emerge. My work focuses on how the brain of a young bird learns to produce each new syllable. My neural recordings and modeling work suggest a simple mechanism by which timing learned in one domain (listening to a tutor) could be transferred to support learning in a new domain (singing).

Stephen Allsop, Tye Lab

Title: A cortico-amygdala circuit encodes observational learning

Abstract: Observational fear learning is a powerful survival tool, allowing an individual to learn about environmental stimuli that predict specific threats without direct experience.  This ability has been conserved from rodents to humans, and has been linked to the anterior cingulate cortex (ACC) and the basolateral amygdala (BLA).  To investigate how information is encoded and transmitted through this network, we recorded from neurons identified as part of the ACC-BLA network to reveal that this network encodes information obtained through observational learning.  We also demonstrate that selective inhibition of the ACC-BLA projection impairs observational fear conditioning and other social behaviors, but not classical fear conditioning. Finally, inhibition of the ACC input to the BLA alters the amygdalar representation of a cue that predicts shock to another mouse. Together, we show that information sourced from observing the experience of another mouse is transmitted from the ACC to the BLA and that this routing of information is necessary for observational fear learn

Rebecca Canter, Tsai Lab

Title: 3D mapping of circuit-specific Alzheimer’s

Abstract: Alzheimer’s disease (AD) is a devastating, progressive loss of memory and cognition for which we have no treatmentsor cures. Decades of research have revealed many molecular phenotypes in AD, yet there is no conclusive link between cellular-level observations and the cognitive symptoms. This may be partly due to our incomplete understanding of the spatiotemporal progressionof the disease and which brain regions are most susceptible to AD-induced dysfunction. Here we use optimized SWITCH techniques to observe molecular changes at the whole-brain level across AD progression in a mouse model of neurodegeneration. We identify novelbrain regions involved in AD and use SWITCH to verify these observations in human patient samples. Using this early susceptibility map, we can begin to probe mechanisms of vulnerability and circuit-level spread, opening new avenues for therapeutic intervention.

Julian Jara-Ettinger, Schulz Lab

Title: Computational principles underlying social cognition

Abstract: As humans we understand that other people have minds and we can infer what they think and what they want by watching their behavior. The computations underlying this ability trace back to early childhood -before children begin kindergarten, and often even in infancy. In this talk I'll discuss the value of a computational theory of social cognition, I'll present developmental studies shedding light on the computations we rely on to make sense of others, and I'll show how a formal model of these computations predicts adult judgments with quantitative precision.