Hebb postulated that, to store information in the brain, assemblies of excitatory neurons
coding for a percept are bound together via associative long-term synaptic plasticity. In this …

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some
responses are so sudden and intense that electrophysiologists felt the need to single them …

Recent approximations to backpropagation (BP) have mitigated many of BP's computational
inefficiencies and incompatibilities with biology, but important limitations still remain …

In theoretical neuroscience, recent work leverages deep learning tools to explore how some
network attributes critically influence its learning dynamics. Notably, initial weight …

Equilibrium propagation (EP) is a compelling alternative to the backpropagation of error
algorithm (BP) for computing gradients of neural networks on biological or analog …

Abstract Models of sensory processing and learning in the cortex need to efficiently assign
credit to synapses in all areas. In deep learning, a known solution is error backpropagation …

Dendrites receive the vast majority of a single neuron's inputs, and coordinate the
transformation of these signals into neuronal output. Ex vivo and theoretical evidence has …

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural
networks (ANNs) have exploited biological properties to solve complex problems. However …

Synapses preferentially respond to particular temporal patterns of activity with a large
degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise …

Normative models of synaptic plasticity use computational rationales to arrive at predictions
of behavioral and network-level adaptive phenomena. In recent years, there has been an …