Sequence memory is an essential attribute of natural and artificial intelligence that enables
agents to encode, store, and retrieve complex sequences of stimuli and actions …

We report a series of experiments on connectionist learning that addresses a particularly
pressing set of objections to the plausibility of connectionist learning as a model of human …

While humans forget gradually, highly distributed connectionist networks forget
catastrophically: newly learned information often completely erases previously learned …

We augment recurrent neural networks with an external memory mechanism that builds
upon recent progress in metalearning. We conceptualize this memory as a rapidly adaptable …

Highlights•Depolarization and neuromodulatory signaling in the amygdala instructs fear
learning.•Midbrain and locus coeruleus pathways convey aversive instructive signals to …

We investigate a new method to augment recurrent neural networks with extra memory
without increasing the number of network parameters. The system has an associative …

Free association among words is a fundamental and ubiquitous memory task. Although
distributed semantics (DS) models can predict the association between pairs of words, and …

Recently, a large number of neural mechanisms and models have been proposed for
sequence learning, of which selfattention, as exemplified by the Transformer model, and …

This paper addresses the nature of the temporary storage buffer used in implicit or statistical
learning. Kuhn and Dienes [Kuhn, G., & Dienes, Z.(2005). Implicit learning of nonlocal …

A major outstanding problem for connectionist models is the representation of variablesized
recursive and sequential data structures, such as trees and stacks, in fixed-resource …