24 citations · 29 across the 5 of their papers we have counts for
5 papers
Neural encoding with affine feature response transforms
Lynn Le, Nils Kimman, Thirza Dado +7
Current linearizing encoding models that predict neural responses to sensory input typically neglect neuroscience-inspired constraints that could enhance model efficiency and inter…
Inverse receptive field attention for naturalistic image reconstruction from the brain
Lynn Le, Thirza Dado, Katja Seeliger +6
Visual perception in the brain largely depends on the organization of neuronal receptive fields. Although extensive research has delineated the coding principles of receptive field…
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…
Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability
Marjolein Troost, Katja Seeliger, Marcel van Gerven
An important issue in neural network research is how to choose the number of nodes and layers such as to solve a classification problem. We provide new intuitions based on earlier…
Deep adversarial neural decoding
Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger +3
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approac…