A review on generative adversarial networks: Algorithms, theory, and applications
Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …
however, they have been studied since 2014, and a large number of algorithms have been …
The neuroconnectionist research programme
Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …
The neural architecture of language: Integrative modeling converges on predictive processing
The neuroscience of perception has recently been revolutionized with an integrative
modeling approach in which computation, brain function, and behavior are linked across …
modeling approach in which computation, brain function, and behavior are linked across …
Integrated information theory (IIT) 4.0: formulating the properties of phenomenal existence in physical terms
This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the
properties of experience in physical (operational) terms. It identifies the essential properties …
properties of experience in physical (operational) terms. It identifies the essential properties …
[HTML][HTML] Integrative benchmarking to advance neurally mechanistic models of human intelligence
A potentially organizing goal of the brain and cognitive sciences is to accurately explain
domains of human intelligence as executable, neurally mechanistic models. Years of …
domains of human intelligence as executable, neurally mechanistic models. Years of …
Driving and suppressing the human language network using large language models
Transformer models such as GPT generate human-like language and are predictive of
human brain responses to language. Here, using functional-MRI-measured brain responses …
human brain responses to language. Here, using functional-MRI-measured brain responses …
If deep learning is the answer, what is the question?
Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …
learning and artificial intelligence research have opened up new ways of thinking about …
Artificial neural networks for neuroscientists: a primer
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …
Direct fit to nature: an evolutionary perspective on biological and artificial neural networks
Evolution is a blind fitting process by which organisms become adapted to their
environment. Does the brain use similar brute-force fitting processes to learn how to …
environment. Does the brain use similar brute-force fitting processes to learn how to …
The origins and prevalence of texture bias in convolutional neural networks
Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify
images by texture rather than by shape. How pervasive is this bias, and where does it come …
images by texture rather than by shape. How pervasive is this bias, and where does it come …