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Found 6 papers, 1 papers with code
Synaptic Diversity in ANNs Can Facilitate Faster Learning
Various advancements in artificial neural networks (ANNs) are inspired by biological concepts, e. g., the artificial neuron, an efficient model of biological nerve cells demonstrating learning capabilities on large amounts of data.
Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian
Developing intelligent neuromorphic solutions remains a challenging endeavour.
Continual Learning with Memory Cascades
This general formulation allows us to use the model also for online learning where no knowledge about task switching times is given to the network.
A synapse-centric account of the free energy principle
The free energy principle (FEP) is a mathematical framework that describes how biological systems self-organize and survive in their environment.
Robust trajectory generation for robotic control on the neuromorphic research chip Loihi
With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales.
Transfer entropy-based feedback improves performance in artificial neural networks
The structure of the majority of modern deep neural networks is characterized by uni- directional feed-forward connectivity across a very large number of layers.
