artificial neural networks

Hyperrealistic neural decoding: Linear reconstruction of face stimuli from fMRI measurements via the GAN latent space

We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by …

DeepRF: Ultrafast population receptive field mapping with deep learning

Population receptive field (pRF) mapping is an important asset for cognitive neuroscience. The pRF model is used for estimating retinotopy, defining functional localizers and to study a vast amount of cognitive tasks. In a classic pRF, the cartesian …

A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time

Visual and auditory representations in the human brain have been studied with encoding, decoding and reconstruction models. Representations from convolutional neural networks have been used as explanatory models for these stimulus-induced …

Population codes of prior knowledge learned through environmental regularities

How the brain makes correct inferences about its environment based on noisy and ambiguous observations, is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment …

Convolutional neural network-based encoding and decoding of visual object recognition in space and time

Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of …

Reconstructing perceived faces from brain activations with deep adversarial neural decoding

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 approach first inverts the linear transformation from latent features to …

Modeling cognitive processes with neural reinforcement learning

Artificial neural networks (ANNs) have seen renewed interest in the fields of computer science, artificial intelligence and neuroscience. Recent advances in improving the performance of ANNs open up an exciting new avenue for cognitive neuroscience …