Forming accurate memory of sequential stimuli is a fundamental function of biological
agents. However, the computational mechanism underlying sequential memory in the brain …

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The
majority of results in AI thus far have been achieved using deep neural networks trained with …

An important difference between brains and deep neural networks is the way they learn.
Nervous systems learn online where a stream of noisy data points are presented in a non …

In this survey, we examine algorithms for conducting credit assignment in artificial neural
networks that are inspired or motivated by neurobiology, unifying these various processes …

We propose a two-stage memory retrieval dynamics for modern Hopfield models, termed
$\mathtt {U\text {-} Hop} $, with enhanced memory capacity. Our key contribution is a …

Bayesian and causal inference are fundamental processes for intelligence. Bayesian
inference models observations: what can be inferred about y if we observe a related variable …

An autoassociative memory model is a function that, given a set of data points, takes as input
an arbitrary vector and outputs the most similar data point from the memorized set. However …

Continual Learning is a burgeoning domain in next-generation AI, focusing on training
neural networks over a sequence of tasks akin to human learning. While CL provides an …

Bayesian and causal inference are fundamental processes for intelligence. Bayesian
inference models observations: what can be inferred about $ y $ if we observe a related …