Evolving developing spiking neural networks

By Diego Federici. 2005

30 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “nce. Attempts to evolve plastic neural controllers extended also to models of spiking neural networks (Maass and Bishop, 2001), which were tested in evolutionary learning experiments (Di Paolo, 2003; Federici, 2005). Spiking neural networks, however, require a more computationally intensive simulati”
Neuroevolution: from architectures to learning (2008, methodology): “Federici [17] combined an integrate and fire neuron model with a correlation-based synaptic plasticity model and a developmental encoding.”
In search of intelligence: evolving a developmental neuron capable of learning (2014, background): “Federici (2005) presented an indirect encoding scheme for the development of a neuro-controller with adaptive rules based on the correlation between postsynaptic electric activity and the local concentration of refractory chemicals.”
Towards evolving spiking networks with memristive synapses (2011, methodology): “A similar spiking model is applied to a simple robotic navigation task [21]; the authors conclude that the temporal capabilities of a SNN may provide further degrees of problem-solving freedom given certain problem types.”
Evolution of Plastic Learning in Spiking Networks via Memristive Connections (2012, methodology): “A similar spiking model is applied to a simple robotic navigation task [26]; the authors concluded that the dynamics of a SNN provide further degrees of problemsolving freedom given temporally-sensitive problems.”
Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture (2011, background): “Federici presented an indirect encoding scheme for development of a neurocontroller (Federici, 2005).”