22 citations · 48 across the 10 of their papers we have counts for
4 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 '…
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov +3
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in bio…
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…