Many recent artificial intelligence (AI) systems take inspiration from biological episodic
memory. Here, we ask how these 'episodic-inspired'AI systems might inform our …

Much of the controversy evoked by the use of deep neural networks as models of biological
neural systems amount to debates over what constitutes scientific progress in neuroscience …

Despite the recent success of neural network models in mimicking animal performance on
various tasks, critics worry that these models fail to illuminate brain function. We take it that a …

Deep learning (DL) has become increasingly central to science, primarily due to its capacity
to quickly, efficiently, and accurately predict and classify phenomena of scientific interest …

" This book provides a framework for thinking about foundational philosophical questions
surrounding machine learning as an approach to artificial intelligence. Specifically, it links …

The last 5 years have seen a series of remarkable achievements in deep-neural-network-
based artificial intelligence research, and some modellers have argued that their …

As a discipline, psychiatry is in the process of finding the right set of concepts to organize
research and guide treatment. Dissatisfaction with the status quo as expressed in standard …

The use of machine learning instead of traditional models in neuroscience raises significant
questions about the epistemic benefits of the newer methods. I draw on the literature on …

The predominance of machine learning based techniques in cognitive neuroscience raises
a host of philosophical and methodological concerns. Given the messiness of neural activity …

This paper aims to clarify the representational status of Deep Learning Models (DLMs).
While commonly referred to as' representations', what this entails is ambiguous due to a …