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 …

Predictive coding offers a potentially unifying account of cortical function--postulating that the
core function of the brain is to minimize prediction errors with respect to a generative model …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

This review article summarises recently proposed theories on how neural circuits in the
brain could approximate the error back-propagation algorithm used by artificial neural …

The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The
recent success of deep networks in machine learning and AI, however, has inspired …

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented
as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking …

Artificial intelligence (AI) is a field of computer science that deals with the simulation of
human intelligence using machines so that such machines gain problem-solving and …

自动目标识别(ATR) 是雷达信息处理领域的重要研究方向. 由于卷积神经网络(CNN)
无需进行特征工程, 图像分类性能优越, 因此在雷达自动目标识别领域研究中受到越来越多的 …