Search Results for author:
Found 6 papers, 2 papers with code
On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing
We anticipate that novel event-based vision datasets can be generated using this pipeline to support various spacecraft pose reconstruction problems given events as input, and we hope that the proposed methodology would attract the attention of researchers working at the intersection of neuromorphic vision and guidance navigation and control.
Neuromorphic Computing and Sensing in Space
The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks.
Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis
Most classical (non-spiking) neural network models disregard internal neuron dynamics and treat neurons as simple input integrators.
Epigenetic evolution of deep convolutional models
In this study, we build upon a previously proposed neuroevolution framework to evolve deep convolutional models.
Adaptive conversion of real-valued input into spike trains
Another merit of the proposed method is that it requires only one input neuron per variable, rather than an entire population of neurons as in the case of the commonly used conversion method based on Gaussian receptive fields.
Complexity-based speciation and genotype representation for neuroevolution
This paper introduces a speciation principle for neuroevolution where evolving networks are grouped into species based on the number of hidden neurons, which is indicative of the complexity of the search space.
