Digital integrated circuits: a design perspective
Digital integrated circuits: a design perspective
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Dynamic stochastic synapses as computational units
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Evolving neural networks through augmenting topologies
Evolutionary Computation
Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments
Evolutionary Computation
Evolvability of Neuromodulated Learning for Robots
LAB-RS '08 Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems
Neural Plasticity and Minimal Topologies for Reward-Based Learning
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Spike-timing-dependent learning in memristive nanodevices
NANOARCH '08 Proceedings of the 2008 IEEE International Symposium on Nanoscale Architectures
Nonvolatile memristor memory: device characteristics and design implications
Proceedings of the 2009 International Conference on Computer-Aided Design
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Evolutionary Design of Neural Network Architectures Using a Descriptive Encoding Language
IEEE Transactions on Evolutionary Computation
Analog Genetic Encoding for the Evolution of Circuits and Networks
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.