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Found 4 papers, 2 papers with code
Optimization of a Hydrodynamic Computational Reservoir through Evolution
In this study, we interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir and optimize its properties using an evolution in materio approach.
Local learning through propagation delays in spiking neural networks
We propose a novel local learning rule for spiking neural networks in which spike propagation times undergo activity-dependent plasticity.
Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity
Neuro-inspired models and systems have great potential for applications in unconventional computing.
On Artificial Life and Emergent Computation in Physical Substrates
We conclude with a philosophical discussion on what we can learn from approaching computation with the curiosity inherent to the study of artificial life.
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