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09/20/2023
The Topology and Geometry of Neural Representations
A central question for neuroscience is how to characterize brain represe...
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06/01/2023
Affinity-based Attention in Self-supervised Transformers Predicts Dynamics of Object Grouping in Humans
The spreading of attention has been proposed as a mechanism for how huma...
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11/28/2022
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Comparing representations of complex stimuli in neural network layers to...
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04/07/2022
Testing the limits of natural language models for predicting human language judgments
Neural network language models can serve as computational hypotheses abo...
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09/07/2021
Capturing the objects of vision with neural networks
Human visual perception carves a scene at its physical joints, decomposi...
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07/01/2021
Visualizing the geometry of labeled high-dimensional data with spheres
Data visualizations summarize high-dimensional distributions in two or t...
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07/06/2020
Comparing representational geometries using the unbiased distance correlation
Representational similarity analysis (RSA) tests models of brain computa...
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03/26/2020
Going in circles is the way forward: the role of recurrence in visual inference
Biological visual systems exhibit abundant recurrent connectivity. State...
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11/21/2019
Controversial stimuli: pitting neural networks against each other as models of human recognition
Distinct scientific theories can make similar predictions. To adjudicate...
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06/21/2019
Visualizing Representational Dynamics with Multidimensional Scaling Alignment
Representational similarity analysis (RSA) has been shown to be an effec...
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03/14/2019
Recurrence required to capture the dynamic computations of the human ventral visual stream
The visual system is an intricate network of brain regions that enables ...
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03/04/2019
Deep Learning for Cognitive Neuroscience
Neural network models can now recognise images, understand text, transla...
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02/13/2019
Neural network models and deep learning - a primer for biologists
Originally inspired by neurobiology, deep neural network models have bec...
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10/06/2018
Adaptive Independence Tests with Geo-Topological Transformation
Testing two potentially multivariate variables for statistical dependenc...
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11/11/2017
Building machines that adapt and compute like brains
Building machines that learn and think like humans is essential not only...
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11/05/2016
Robustly representing inferential uncertainty in deep neural networks through sampling
As deep neural networks (DNNs) are applied to increasingly challenging p...
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11/12/2015
