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Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks
We show how a multi-agent system (ant colony) steered by Spiking Neural Networks establishes self-organization while foraging for food. We evolve the networks using a genetic algorithm and learning to learn. Our results depict emergent behavior, which we investigate.
Cristian Jimenez Romero, Alper Yegenoglu, Aarón Pérez Martı́n, Sandra Diaz-Pier, Abigail Morrison
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Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks
Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn
Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to …
Alper Yegenoglu, Anand Subramoney, Thorsten Hater, Cristian Jimenez Romero, Wouter Klijn, Aaron Pérez Martín, Michiel van der Vlag, Michael Herty, Abigail Morrison, Sandra Diaz-Pier
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Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn
Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks
Cristian Jimenez Romero, Alper Yegenoglu, Aarón Pérez Martı́n, Sandra Diaz-Pier, Abigail Morrison
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Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent
The successful training of deep neural networks is dependent on initialization schemes and choice of activation functions. …
Alper Yegenoglu, Kai Krajsek, Sandra Diaz-Pier, Michael Herty
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Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent