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 …

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

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well
established that it depends on pre-and postsynaptic activity. However, models that rely …

It is a fundamental challenge to understand how the function of a network is related to its
structural organization. Adaptive dynamical networks represent a broad class of systems that …

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 …

We introduce Equilibrium Propagation, a learning framework for energy-based models. It
involves only one kind of neural computation, performed in both the first phase (when the …

Most elementary behaviors such as moving the arm to grasp an object or walking into the
next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal …

In spike-timing-dependent plasticity (STDP), the order and precise temporal interval
between presynaptic and postsynaptic spikes determine the sign and magnitude of long …

Neuroscientists have long criticised deep learning algorithms as incompatible with current
knowledge of neurobiology. We explore more biologically plausible versions of deep …