Multistage STM in a Multilayer Hebbian Learning Architecture for Local Navigation

Effects of Temperature on Radiative Properties of Nanoscale Multilayer with Coherent Formulation in Visible Wavelengths

During the past two decades, there have been tremendous developments in near-field imaging and local probing techniques. Examples are the Scanning Tunneling Microscope (STM), Atomic Force Microscope (AFM), Near-field Scanning Optical Microscope (NSOM), Photon Scanning Tunneling Microscope (PSTM), and Scanning Thermal Microscope (SThM).Results showed that the average reflectance for a dopant con...

Hebbian Multilayer Network in a Wheelchair Robot

Motivation to study Hebbian learning arises from its neu-rophysiological plausibility and its suitability for hardware implementation. Up to now, artiicial Hebbian learning, embedded in a real system that performs adaptive motor control, has been restricted to one-layer networks. To overcome this limitation, a novel approach to adaptive pre-processing based on Hebbian learning is presented. It ...

Real-time Hebbian Learning from Autoencoder Features for Control Tasks

Neural plasticity and in particular Hebbian learning play an important role in many research areas related to artficial life. By allowing artificial neural networks (ANNs) to adjust their weights in real time, Hebbian ANNs can adapt over their lifetime. However, even as researchers improve and extend Hebbian learning, a fundamental limitation of such systems is that they learn correlations betw...

Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network

Backpropagation and contrastive Hebbian learning are two methods of training networks with hidden neurons. Backpropagation computes an error signal for the output neurons and spreads it over the hidden neurons. Contrastive Hebbian learning involves clamping the output neurons at desired values and letting the effect spread through feedback connections over the entire network. To investigate the...

A multistage on-line learning rule for multilayer neural network