Search Results for author:
Found 5 papers, 2 papers with code
Sparsifying Spiking Networks through Local Rhythms
It has been well-established that within conventional neural networks, many of the values produced at each layer are zero.
Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks
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 'phasor.'
Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations
These include high energy consumption, catastrophic forgetting, dependance on global losses, and an inability to reason symbolically.
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms
Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications.
Stochasticity and Robustness in Spiking Neural Networks
We then train spiking networks which utilize IF neurons with and without noise and leakage, and experimentally confirm that the noisy networks are more robust.
