Cortical Interactions in the Oculomotor System
Abstract:
January 17 (virtual) - Sara Solla (NorthWestern)
Title: Low Dimensional Manifolds for Neural Dynamics
February 20 (virtual) - Eric Shea-Brown (Washington)
Title: When do high dimensional networks learn to produce low dimensional dynamics?
March 21 *IN PERSON* E5-2004 - Maurizio de Pitta (Krembil/UofT)*
Title: Neuron-glial switches
April 25 14:30 *In Person*
Speaker: Jeff Orchard (CS, Waterloo)
Title: Cognition using Spiking-Phasor Neurons
Prof. Eva Dyer (home page) will present on her work on Thursday, March 6, 3:30 p.m. in E5 2004.
Scaling Up Neural Data Pretraining to Uncover Shared Structure in Brain Function
The brain is incredibly complex, with diverse functions that emerge from the coordinated activity of billions of neurons. These functions vary across brain regions and adapt dynamically as we engage in different tasks, process sensory information, or generate behavior. Yet, each neural recording captures only a small glimpse of this immense complexity, offering a limited view of the broader system. This motivates the need for an algorithmic approach to stitch together diverse datasets, integrating neural activity across brain regions, cell types, and individuals. In this talk, I will present our work on building scalable models pretrained on a broad corpus of neural recordings. Our findings demonstrate positive transfer across tasks, cell types, and individuals, effectively bridging gaps between isolated studies. This unified framework opens new possibilities for neural decoding, brain-machine interfaces, and cross-species neuroscience, offering a path toward more generalizable models of brain function.
Mark Reimers, Michigan State (https://iq.msu.edu/mark-reimers/)
Location: E5 2004
Title: A new and inexpensive method for high-resolution imaging of neural activity across the cortex of small animals
Abstract: In this talk I will introduce a new system for imaging the activity of several thousand labelled neurons distributed sparsely across the dorsal cortex of a mouse at high speed. The key is to use extensive computation to make up for the deficits of simple imaging systems. I will describe the ideas behind our system and the technology that we're using to implement these ideas, at a cost of under $50,000. I will describe some of the technical issues we've addressed, and issues that we’re still working on. A natural question to ask is how much of the complex cortical activity can be inferred by recording from a small fraction of neurons in each area. I will present evidence from large-scale Zebrafish and mouse brain recordings to suggest that a surprisingly small fraction of labelled neurons may be sufficient to represent most of the population activity in the upper layers of cortex.