Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics
through mechanisms of synaptic plasticity. Inhibitory plasticity can establish …

Recurrent neural networks (RNNs) trained with machine learning techniques on cognitive
tasks have become a widely accepted tool for neuroscientists. In this short opinion piece, we …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …

There is substantial experimental evidence that learning and memory-related behaviours
rely on local synaptic changes, but the search for distinct plasticity rules has been driven by …

Over the course of a lifetime, we process a continual stream of information. Extracted from
this stream, memories must be efficiently encoded and stored in an addressable manner for …

The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational
neuroscience and has been instrumental in studying our brains $\textit {in silico} $. Due to …

Continuous adaptation allows survival in an ever-changing world. Adjustments in the
synaptic coupling strength between neurons are essential for this capability, setting us apart …

One challenge of physics is to explain how collective properties arise from microscopic
interactions. Indeed, interactions form the building blocks of almost all physical theories and …

Backpropagation is widely used to train artificial neural networks, but its relationship to
synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely …