Upcoming Events!
Abstract: “A central goal shared by neuroscience and robotics is to understand how systems can navigate and act autonomously in complex environments. Although extensive research has revealed how the visual system segments natural scenes into distinct components—insights that have inspired advances in computer vision and robotics—the next crucial challenge remains: learning the properties of these objects and responding appropriately. In this talk, I will present our work using the fruit fly Drosophila melanogaster to investigate how the brain learns about objects and other animals in its environment, and how it uses that information to guide behavior. By integrating quantitative behavioral analysis, genetic manipulation, connectomics, and neural recordings, we aim to uncover the neural mechanisms that enable flexible, adaptive interactions with the world.”
Abstract
Around 380 million years ago, as vertebrates ventured onto land, vision changed dramatically. Air is far more transparent than water, and at the water-to-land transition we also see a marked increase in eye size. Together these factors yielded an enormous extension of visual range, resulting in a million-fold expansion in the volume of space that could be visually monitored. With objects detectable much farther away, animals suddenly had more time to act.
I argue that this elongated sensory horizon shifted the advantage from fast, reflexive responses---effective underwater when threats emerge at about a body length---to multi-step action sequences that are planned ahead. In particular, partially cluttered terrestrial settings (e.g., savanna-like mixes of open zones and cover) create many viable future paths, some of which will avoid mortal threat while others will lead to death. In such environments, selecting among imagined futures---planning---should pay off.
I will first present motivating simulation results showing that planning yields large benefits specifically in mid-clutter terrestrial regimes, whereas in very simple or very cluttered spaces habit-based action control is equally or more effective. To test these predictions behaviorally, we've built a robot–rodent interaction arena with reconfigurable obstacles that let us dial spatial complexity up or down. An autonomous robot acts as a mobile threat while mice navigate toward safety. I will share initial behavioral findings from this paradigm, including path diversification and pauses that appear to support look-ahead, as well as preliminary hippocampal recordings acquired during behavior. We also show some initial work comparing state-of-the-art reinforcement learning algorithms to animal behavior with interesting implications for improving AI. Together, these results outline a tractable experimental program for linking expanded terrestrial sensory horizons to the emergence of planning---and, potentially, to key components of mind.
Past Events
This is a reschedule remote seminar for Eugenia's seminar from May 8. Contact ionseminars@uoregon...
This is a reschedule remote seminar for Eugenia's seminar from May 8. Contact ionseminars@uoregon.edu to receive the zoom link if not on regular mailing lists with your uoregon email address.
This visit is postponed for 5/29 and we hope to reschedule in the 2025-26 academic year.
Abstract -...
This visit is postponed for 5/29 and we hope to reschedule in the 2025-26 academic year.
Abstract - The superior colliculus (SC) is an evolutionarily conserved structure that receives direct retinal input in all vertebrates. It was the most sophisticated visual center until the neocortex evolved in mammals. Even in mice and tree shrews, mammalian species that are increasingly used in vision research, the vast majority of retinal ganglion cells project to the SC, making it a prominent visual structure in these animals. In this talk, I will review our recent functional studies of the mouse SC and describe our current efforts in linking functional properties to genetically identified cell types in both mice and tree shrews.
2025 UO Undergraduate Research Symposium
Undergraduates, register by April 17, 2025 to present!
2025 UO Undergraduate Research Symposium
Undergraduates, register by April 17, 2025 to present!
A fundamental challenge to understanding the brain is its complexity: its genetic and developmental...
A fundamental challenge to understanding the brain is its complexity: its genetic and developmental programs, its neurons and connections, its balance of permanence and plasticity, and the nuanced information flow through its networks. Across biology, emerging technologies are revolutionising the scope and scale at which we can address such questions. Our group has developed technologies for studying brain-wide sensory networks using calcium imaging and house-built light-sheet microscopes. Because zebrafish larvae are small and transparent, we can image tens of thousands of neurons, simultaneously and individually, as animals perceive and respond to sensory stimuli. We have used this approach to produce the first functional maps, brain-wide at cellular resolution, for auditory, vestibular, and water flow perception in a vertebrate. Our lab and others have also used such baseline descriptions as a departure point for exploring altered sensory networks in zebrafish models of neuropsychiatric conditions such as autism.
These imaging studies have taught us where and when neurons are active, but not how or why. In this talk, I will review our calcium imaging approaches and results, and will then discuss how other technologies, such as X-ray diffraction, electron microscopy, and spatial transcriptomics can provide complementary information about the structure and function of brain-wide networks. I will present preliminary data using these platforms and discuss the enormous potential that such combined approaches hold, but also the technical challenges that merging these big-data modalities presents.
Abstract: Bats are highly social animals with complex vocal communication systems supporting...
Abstract: Bats are highly social animals with complex vocal communication systems supporting navigation and social interactions. My research investigates the neural and behavioral mechanisms underlying social cohesion, auditory perception, and communication in bats. From a neuropathological perspective, integrating behavioral ecology with neural systems approaches, my lab explores fundamental questions such as how bats recognize roost mates and how this may influence roost fidelity, how hierarchical social structures form, and how context shapes responses to communication sounds. To explore these questions we employ a multidisciplinary approach, combining behavioral assays, electrophysiological recordings, neuroanatomical mapping, and computational modeling; spanning both controlled laboratory settings and field environments. In this talk, I will provide an overview of our recent advances and strategies to answer these questions and discuss how our work provides key insights into the neural mechanisms and behavioral ecology of auditory communication in mammals.
In this talk, I will examine the computational motivations and empirical evidence for spatiotemporal...
In this talk, I will examine the computational motivations and empirical evidence for spatiotemporal dopamine (DA) waves that support reward learning within fronto-striatal networks. I will focus on the cognitive striatum as a case study to show that DA waves tailor decision signals according to local computational/behavioral specialty-- accomplished via vector-weighting delays in DA pulses across space and time. This code resolves key computational challenges in competing C-BG mixture of experts: spatiotemporal credit assessment at reward, and dynamic reprioritization of circuit inference and gating during performance. Ultimately, these DA wave dynamics represent an empirically informed revision of the longstanding "global broadcast" hypothesis of DA RPE signals. Finally, I will briefly summarize our recent attempts at understanding the complexity of the DA wave manifold, and competitive/collaborative circuit interactions that constrain DA to motif trajectories during specific task demands.