Systems neuroscience has reached the stage where computational models now represent real predictive models that can: a) perform tasks similar to those performed by animals; b) learn from experience, and c) exhibit internal states that are often significantly similar to neural activities within the brain. At the same time, experimental tests of these models have been relatively limited to only individual small-scale datasets, and it remains unclear which model candidates are best supported by data. This workshop sets the stage for experimenters, theorists, and everyone in between, to objectively discuss the landscape of approaches and the best next steps. By assembling a diverse group of speakers, spanning research disciplines and career stages, we aim to help the field leverage the recent advances in AI to drive progress in neuroscience.

Who is our target audience? Our target audience is broad: all systems neuroscientists who are skeptical of current modeling frameworks in neuroscience and those that are interested in applying these frameworks and leading brain models to their research. This includes a diverse group of theorists, experimenters, and everyone in between at different career stages and geographic locations.