Alish Dipani

Braintree autonomously runs in-silico vision neuroscience experiments to test hypotheses, generate stimuli, and analyze neural responses using brain models.

The paper introduces a zero-shot evaluation framework to assess predictive models of the brain, revealing structured generalization limits and linking prediction performance to cognitive neuroscience findings.

The paper compares category selectivity in human brain regions with that in artificial neural networks, finding significant differences in response tuning and stability.