Spike-timing error backpropagation in theta neuron networks
Neural Computation
Towards a novel immune inspired approach to temporal anomaly detection
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Evolutionary multi-objective optimization of spiking neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Spatio-temporal spike pattern classification in neuromorphic systems
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Hi-index | 0.00 |
This paper presents new findings in the design and application of biologically plausible neural networks based on spiking neuron models, which represent a more plausible model of real biological neurons where time is considered as an important feature for information encoding and processing in the brain. The design approach consists of an evolutionary strategy based supervised training algorithm, newly developed by the authors, and the use of different biologically plausible neuronal models. A dynamic synapse (DS) based neuron model, a biologically more detailed model, and the spike response model (SRM) are investigated in order to demonstrate the efficacy of the proposed approach and to further our understanding of the computing capabilities of the nervous system. Unlike the conventional synapse, represented as a static entity with a fixed weight, employed in conventional and SRM-based neural networks, a DS is weightless and its strength changes upon the arrival of incoming input spikes. Therefore its efficacy depends on the temporal structure of the impinging spike trains. In the proposed approach, the training of the network free parameters is achieved using an evolutionary strategy where, instead of binary encoding, real values are used to encode the static and DS parameters which underlie the learning process. The results show that spiking neural networks based on both types of synapse are capable of learning non-linearly separable data by means of spatio-temporal encoding. Furthermore, a comparison of the obtained performance with classical neural networks (multi-layer perceptrons) is presented.