The on-chip implementation of learning algorithms would speed up the training of neural
networks in crossbar arrays. The circuit level design and implementation of a back …

Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and
central pattern generator circuits that underlie rhythmic behaviors like locomotion. While …

Spiking activity of neurons engaged in learning and performing a task show complex
spatiotemporal dynamics. While the output of recurrent network models can learn to perform …

Combinatorial threshold-linear networks (CTLNs) are a special class of inhibition-dominated
TLNs defined from directed graphs. Like more general TLNs, they display a wide variety of …

Threshold-linear networks (TLNs) are models of neural networks that consist of simple,
perceptron-like neurons and exhibit nonlinear dynamics determined by the network's …

There is enormous interest--both mathematically and in diverse applications--in
understanding the dynamics of coupled oscillator networks. The real-world motivation of …

Threshold-linear networks consist of simple units interacting in the presence of a threshold
nonlinearity. Competitive threshold-linear networks have long been known to exhibit …

Threshold-linear networks are a common class of firing rate models that describe recurrent
interactions among neurons. Unlike their linear counterparts, these networks generically …

Threshold-linear networks (TLNs) are recurrent networks where the dynamics are threshold-
linear (linearly rectified at zero). Mathematically, they consist of coupled nonsmooth ordinary …

Threshold-linear networks consist of simple units interacting in the presence of a threshold
nonlinearity. Competitive threshold-linear networks have long been known to exhibit …