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 …

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information
processing that are inspired by neurobiological systems, and this feature distinguishes …

Since the beginning of information processing by electronic components, the nervous
system has served as a metaphor for the organization of computational primitives. Brain …

Neuromorphic computing systems comprise networks of neurons that use asynchronous
events for both computation and communication. This type of representation offers several …

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 …

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 …

This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing
and disparate experimental datasets at multiple scales. The NetPyNE tool (www. netpyne …

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

We present a full custom hardware implementation of a deep neural network, built using
multiple neuromorphic VLSI devices that integrate analog neuron and synapse circuits …