6 accepted papers
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve generalizable, cross-subject models. A major obstacle towards this g
Recent work has demonstrated that complex visual stimuli can be decoded from human brain activity using deep generative models, helping brain science researchers interpret how the brain represents real-world scenes. However, most current approaches leverage mapping brain signals into intermediate im…
Deep neural networks have been criticized as fundamentally *statistical* systems that fail to capture causal structure and perform causal reasoning. Here we demonstrate that a GPT-style transformer trained for next-token prediction can simultaneously discover instances of linear Gaussian structural…
A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies using designed experiments have identified multiple category-selective regions; however, these approaches rely on preconce…
In classification tasks, examples belonging to the same class can often still differ substantially from one another, and being able to capture such heterogeneity and its impact on classification can be important for aggregating estimates across multiple classifiers. Bayesian models developed so far
A major goal of neuroscience is to understand brain computations during visual processing in naturalistic settings. A dominant approach is to use image-computable deep neural networks trained with different task objectives as a basis for linear encoding models. However, in addition to requiring esti…