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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…
Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations
Ahmed ElGazzar, Marcel van Gerven
We propose a probabilistic framework for developing computational models of biological neural systems. In this framework, physiological recordings are viewed as discrete-time parti…
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…
Modulation of early visual processing alleviates capacity limits in solving multiple tasks
Sushrut Thorat, Giacomo Aldegheri, Marcel A. J. van Gerven +1
In daily life situations, we have to perform multiple tasks given a visual stimulus, which requires task-relevant information to be transmitted through our visual system. When it i…
The functional role of cue-driven feature-based feedback in object recognition
Sushrut Thorat, Marcel van Gerven, Marius Peelen
Visual object recognition is not a trivial task, especially when the objects are degraded or surrounded by clutter or presented briefly. External cues (such as verbal cues or visua…