21 citations · 30 across the 12 of their papers we have counts for
3 papers · 1 filter
Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations
Wilkie Olin-Ammentorp, Maxim Bazhenov
Despite rapid progress, current deep learning methods face a number of critical challenges. These include high energy consumption, catastrophic forgetting, dependance on global los…
Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks
Wilkie Olin-Ammentorp, Maxim Bazhenov
In this work, we extend standard neural networks by building upon an assumption that neuronal activations correspond to the angle of a complex number lying on the unit circle, or '…
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms
Wilkie Olin-Ammentorp, Yury Sokolov, Maxim Bazhenov
Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applic…