Neuro-evolution of spiking neural networks on SpiNNaker neuromorphic hardware
| Autor: | Florian Röhrbein, Alexander Vandesompele, Florian Walter |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Spiking neural network
Fitness function Artificial neural network SpiNNaker business.industry Computer science 02 engineering and technology Network topology Network simulation 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Types of artificial neural networks business Adaptation (computer science) 030217 neurology & neurosurgery |
| Zdroj: | SSCI |
| DOI: | 10.1109/ssci.2016.7850250 |
| Popis: | Neuro-evolutionary algorithms optimize the synaptic connectivity of sets of candidate neural networks based on a task-dependent fitness function. Compared to the commonly used methods from machine learning, many of them not only support the adaptation of connection weights but also of the network topology. However, the evaluation of the current fitness requires running every candidate network in every generation. This becomes a major impediment especially when using biologically inspired spiking neural networks which require considerable amounts of simulation time even on powerful computers. In this paper, we address this issue by offloading the network simulation to SpiNNaker, a state-of-the art neuromorphic hardware architecture which is capable of simulating large spiking neural networks in biological real-time. We were able to apply SpiNNaker's simulation power to the popular NEAT algorithm by running all candidate networks in parallel and successfully evolved spiking neural networks for solving the XOR problem and for playing the Pac-Man arcade game |
| Databáze: | OpenAIRE |
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