Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

The neuronal circuits of the basolateral amygdala (BLA) are crucial for acquisition,
consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Conclusion Neural networks are experiencing a revival that not only transforms AI but also
provides new insights about neural computation in biological systems. The contributions in …

We propose a new supervised learning rule for multilayer spiking neural networks (SNNs)
that use a form of temporal coding known as rank-order-coding. With this coding scheme, all …

Neural activity is often described in terms of population-level factors extracted from the
responses of many neurons. Factors provide a lower-dimensional description with the aim of …

Beyond energy, the growing number of defects in physical substrates is becoming another
major constraint that affects the design of computing devices and systems. As the underlying …

The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …

“Neural coding” is a popular metaphor in neuroscience, where objective properties of the
world are communicated to the brain in the form of spikes. Here I argue that this metaphor is …

Lifelong learning and adaptability are two defining aspects of biological agents. Modern
reinforcement learning (RL) approaches have shown significant progress in solving complex …