In this paper, we present a novel deep unsupervised active learning framework. The proposed method can explicitly learn a nonlinear embedding to map each input into a latent space via a deep neural ...

What people tend to overlook, ignore, or forget, is that each “node” in a layer is a block of code ... in supervised or unsupervised learning modes, when a neural network learning pass ...

Spiking Neural Networks (SNNs) offer great potential to promote both ... The SNNs could utilize Spike-Timing-Dependent Plasticity (STDP) as the unsupervised learning rule, and this plasticity has been ...

The desire to build and operate large neural networks ... are capable of learning and re-learning of input patterns in an unsupervised manner within a probabilistic WTA network.

linking discriminative vision tasks and unsupervised feature extraction. Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding ...

These results illustrate a use of unsupervised learning to model a brain system and present a strong candidate for a biologically plausible computational theory of sensory learning. Deep neural ...