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 …

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 …

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 …

Backpropagation (BP) uses detailed, unit-specific feedback to train deep neural networks
(DNNs) with remarkable success. That biological neural circuits appear to perform credit …

The search for biologically faithful synaptic plasticity rules has resulted in a large body of
models. They are usually inspired by--and fitted to--experimental data, but they rarely …

Deep learning models trained on large data sets have been widely successful in both vision
and language domains. As state-of-the-art deep learning architectures have continued to …

Optimization of non-convex loss surfaces containing many local minima remains a critical
problem in a variety of domains, including operations research, informatics, and material …

Spiking Neural Networks (SNNs) have recently gained popularity as machine learning
models for on-device edge intelligence for applications such as mobile healthcare …

Learning to perform a perceptual decision task is generally achieved through sessions of
effortful practice with feedback. Here, we investigated how passive exposure to task-relevant …