activity
20182022
most citedNeural network models and deep learning - a primer for biologists

553 citations · 592 across the 7 of their papers we have counts for

collaborators

15 papers

q-bio.NC20224 cited

Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments

Tal Golan, Wenxuan Guo, Heiko H. Schütt +1

Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitativel…

q-bio.NC202224 cited

The neuroconnectionist research programme

Adrien Doerig, Rowan Sommers, Katja Seeliger +8

Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism. ANNs have been la…

q-bio.NC2022

Inferring exemplar discriminability in brain representations

Hamed Nili, Alexander Walther, Arjen Alink +1

Representational distinctions within categories are important in all perceptual modalities and also in cognitive and motor representations. Recent pattern-information studies of br…

q-bio.NC2021

Capturing the objects of vision with neural networks

Benjamin Peters, Nikolaus Kriegeskorte

Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked, and predicted as we engage our surroundi…

stat.CO2021

Visualizing the geometry of labeled high-dimensional data with spheres

Andrew D Zaharia, Anish S Potnis, Alexander Walther +1

Data visualizations summarize high-dimensional distributions in two or three dimensions. Dimensionality reduction entails a loss of information, and what is preserved differs betwe…

q-bio.NC20218 cited

Neural tuning and representational geometry

Nikolaus Kriegeskorte, Xue-Xin Wei

A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behavior. The classical approach is to investigate how…