9 citations · 25 across the 8 of their papers we have counts for
6 papers · 1 filter
Energy efficiency analysis of Spiking Neural Networks for space applications
Paolo Lunghi, Stefano Silvestrini, Dominik Dold +3
While the exponential growth of the space sector and new operative concepts ask for higher spacecraft autonomy, the development of AI-assisted space systems was so far hindered by…
Neuromorphic Computing and Sensing in Space
Dario Izzo, Alexander Hadjiivanov, Dominik Dold +2
The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks. Typical examples are novel computer chip…
Epigenetic evolution of deep convolutional models
Alexander Hadjiivanov, Alan Blair
In this study, we build upon a previously proposed neuroevolution framework to evolve deep convolutional models. Specifically, the genome encoding and the crossover operator are ex…
Adaptive conversion of real-valued input into spike trains
Alexander Hadjiivanov
This paper presents a biologically plausible method for converting real-valued input into spike trains for processing with spiking neural networks. The proposed method mimics the a…
Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis
Alexander Hadjiivanov
Most classical (non-spiking) neural network models disregard internal neuron dynamics and treat neurons as simple input integrators. However, biological neurons have an internal st…
Complexity-based speciation and genotype representation for neuroevolution
Alexander Hadjiivanov, Alan Blair
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 c…