12 citations · 17 across the 3 of their papers we have counts for
6 papers
Learning Coherent Representations: A Topological Approach to Interpretability
Sigurd Gaukstad, Melvin Vaupel, Valdemar Kargård Olsen +2
Deep neural networks learn representations where individual features often lack interpretable meaning; a single neuron may activate for scattered, unrelated inputs. We introduce co…
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
Martin Bjerke, Lukas Schott, Kristopher T. Jensen +3
Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives comb…
Score-Based Generative Classifiers
Roland S. Zimmermann, Lukas Schott, Yang Song +2
The tremendous success of generative models in recent years raises the question whether they can also be used to perform classification. Generative models have been used as adversa…
Decoding of neural data using cohomological feature extraction
Erik Rybakken, Nils Baas, Benjamin Dunn
We introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomolo…
Grid cells show field-to-field variability and this explains the aperiodic response of inhibitory interneurons
Benjamin Dunn, Daniel Wennberg, Ziwei Huang +1
Research on network mechanisms and coding properties of grid cells assume that the firing rate of a grid cell in each of its fields is the same. Furthermore, proposed network model…
The appropriateness of ignorance in the inverse kinetic Ising model
Benjamin Dunn, Claudia Battistin
We develop efficient ways to consider and correct for the effects of hidden units for the paradigmatic case of the inverse kinetic Ising model with fully asymmetric couplings. We i…