71 citations · 162 across the 9 of their papers we have counts for
9 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…
Using persistent homology to reveal hidden information in neural data
Gard Spreemann, Benjamin Dunn, Magnus Bakke Botnan +1
We propose a method, based on persistent homology, to uncover topological properties of a priori unknown covariates of neuron activity. Our input data consist of spike train measur…
Correlations and functional connections in a population of grid cells
Benjamin Dunn, Maria Mørreaunet, Yasser Roudi
We study the statistics of spike trains of simultaneously recorded grid cells in freely behaving rats. We evaluate pairwise correlations between these cells and, using a generalize…
Learning and inference in a nonequilibrium Ising model with hidden nodes
Benjamin Dunn, Yasser Roudi
We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configu…