Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

The experimental study of learning and plasticity has always been driven by an implicit
question: how can physiological changes be adaptive and improve performance? For …

There is an increasing interest in emulating Spiking Neural Networks (SNNs) on
neuromorphic computing devices due to their low energy consumption. Recent advances …

Learning in the brain is poorly understood and learning rules that respect biological
constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose …

Equilibrium systems are a powerful way to express neural computations. As special cases,
they include models of great current interest in both neuroscience and machine learning …

With recent advances in learning algorithms, recurrent networks of spiking neurons are
achieving performance that is competitive with vanilla recurrent neural networks. However …

Covert attention allows the selection of locations or features of the visual scene without
moving the eyes. Cues and contexts predictive of a target's location orient covert attention …

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 …

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 …

Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of
shortcomings with regards to different application domains and goals. These limitations are …