During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses
are embedded within multilayered networks, making it difficult to determine the effect of an …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate
advanced electronic circuits is a major goal for the semiconductor industry,. However, most …

What happens in our brain when we make a decision? What triggers a neuron to send out a
signal? What is the neural code? This textbook for advanced undergraduate and beginning …

Neurons in the brain communicate by short electrical pulses, the so-called action potentials
or spikes. How can we understand the process of spike generation? How can we …

Hebbian models of development and learning require both activity-dependent synaptic
plasticity and a mechanism that induces competition between different synapses. One form …

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of
neural activity in the brain-wide network. This review focuses on oscillations in the cerebral …

Synaptic plasticity provides the basis for most models of learning, memory and development
in neural circuits. To generate realistic results, synapse-specific Hebbian forms of plasticity …

Most of our knowledge about the functional organization of neuronal systems is based on
the analysis of the firing patterns of individual neurons that have been recorded one by one …

Inspired by detailed modelling of biological neurons, spiking neural networks (SNNs) are
investigated as biologically plausible and high-performance models of neural computation …