Episodic memories are (re) constructed, share neural substrates with imagination, combine
unique features with schema-based predictions and show schema-based distortions that …

We introduce an Outlier-Efficient Modern Hopfield Model (termed $\mathtt {OutEffHop} $)
and use it to address the outlier-induced challenge of quantizing gigantic transformer-based …

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

We introduce the\textbf {B} i-Directional\textbf {S} parse\textbf {Hop} field Network (\textbf
{BiSHop}), a novel end-to-end framework for deep tabular learning. BiSHop handles the two …

Empirical data, on which deep learning relies, has substantial internal structure, yet
prevailing theories often disregard this aspect. Recent research has led to the definition of …

The brain is targeted for processing temporal sequence information. It remains largely
unclear how the brain learns to store and retrieve sequence memories. Here, we study how …

Sequential memory, the ability to form and accurately recall a sequence of events or stimuli
in the correct order, is a fundamental prerequisite for biological and artificial intelligence as it …

Understanding the intricate operations of Recurrent Neural Networks (RNNs)
mechanistically is pivotal for advancing their capabilities and applications. In this pursuit, we …

Content-addressable memories such as Modern Hopfield Networks (MHN) have been
studied as mathematical models of auto-association and storage/retrieval in the human …

Episodic memory is often illustrated with the madeleine de Proust excerpt as the ability to re-
experience a situation from the past following the perception of a stimulus. This simplistic …