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Found 5 papers, 3 papers with code
FEDORA: Flying Event Dataset fOr Reactive behAvior
The ability of resource-constrained biological systems such as fruitflies to perform complex and high-speed maneuvers in cluttered environments has been one of the prime sources of inspiration for developing vision-based autonomous systems.
Event-based Temporally Dense Optical Flow Estimation with Sequential Learning
In this work, we show that a temporally dense flow estimation at 100Hz can be achieved by treating the flow estimation as a sequential problem using two different variants of recurrent networks - Long-short term memory (LSTM) and spiking neural network (SNN).
Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning
We show that SNNs can be trained for sequential tasks and propose modifications to a network of LIF neurons that enable internal states to learn long sequences and make their inherent recurrence resilient to the vanishing gradient problem.
Reinforcement Learning with Low-Complexity Liquid State Machines
We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters.
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks
In this work, we present, for the first time, a comprehensive analysis of the behavior of more bio-plausible networks, namely Spiking Neural Network (SNN) under state-of-the-art adversarial tests.
