Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

Neuromorphic hardware enables fast and power-efficient neural network–based artificial
intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …

The unprecedented progress in artificial intelligence (AI), particularly in deep learning
algorithms with ubiquitous internet connected smart devices, has created a high demand for …

To navigate in new environments, an animal must be able to keep track of its position while
simultaneously creating and updating an internal map of features in the environment, a …

Solving constraint satisfaction problems (CSPs) is in high demand for various applications.
SNN serves as a competitive annealing machine that can solve the CSP more efficiently …

The Abisko project aims to develop an energy-efficient spiking neural network (SNN)
computing architecture and software system capable of autonomous learning and operation …

In this paper, we focus on the hand construction of small-scale, spiking, neuromorphic
networks. They are partitioned into two sets. The first set performs the binary operations …