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Generative Adversarial Collaborations: A practical guide for conference organizers and participating scientists
Generative adversarial collaborations (GACs) are a form of formal teamwork between groups of scientists with diverging views.
How does the primate brain combine generative and discriminative computations in vision?
The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it.
Beyond linear regression: mapping models in cognitive neuroscience should align with research goals
Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Disentangled Face Representations in Deep Generative Models and the Human Brain
We further find that the latent dimensions in these models map onto non-overlapping regions in fMRI data, allowing us to "disentangle" different features such as 3D rotation, skin tone, and facial expression in the human brain.
Computational role of eccentricity dependent cortical magnification
From the slope of the inverse of the magnification factor, M-theory predicts a cortical "fovea" in V1 in the order of $40$ by $40$ basic units at each receptive field size -- corresponding to a foveola of size around $26$ minutes of arc at the highest resolution, $\approx 6$ degrees at the lowest resolution.
