We review a body of theoretical and experimental research on Hebbian and homeostatic
plasticity, starting from a puzzling observation: while homeostasis of synapses found in …

Recent experiments have revealed that neural population codes in many brain areas
continuously change even when animals have fully learned and stably perform their tasks …

It is widely believed that end-to-end training with the backpropagation algorithm is essential
for learning good feature detectors in early layers of artificial neural networks, so that these …

Training deep neural networks with the error backpropagation algorithm is considered
implausible from a biological perspective. Numerous recent publications suggest elaborate …

Modeling self-organization of neural networks for unsupervised learning using Hebbian and
anti-Hebbian plasticity has a long history in neuroscience. Yet derivations of single-layer …

Inventors of the original artificial neural networks (ANNs) derived their inspiration from
biology [1]. However, today, most ANNs, such as backpropagation-based convolutional …

Neurons in piriform cortex receive input from a random collection of glomeruli, resulting in
odor representations that lack the stereotypic organization of the olfactory bulb. We have …

Hebbian plasticity in winner-take-all (WTA) networks is highly attractive for neuromorphic on-
chip learning, owing to its efficient, local, unsupervised, and on-line nature. Moreover, its …

Both vertebrates and invertebrates perceive illusory motion, known as" reverse-phi," in
visual stimuli that contain sequential luminance increments and decrements. However …

Many neurons in the brain, such as place cells in the rodent hippocampus, have localized
receptive fields, ie, they respond to a small neighborhood of stimulus space. What is the …