Neural networks with dynamic synapses
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Learning Temporally Encoded Patterns in Networks of SpikingNeurons
Neural Processing Letters
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Inference for the Generalization Error
Machine Learning
Neural Computation
Receptive field optimization for ensemble encoding
Neural Computing and Applications
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Neural Systems as Nonlinear Filters
Neural Computation
Implementing Fuzzy Reasoning on a Spiking Neural Network
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Optimal Hebbian learning: a probabilistic point of view
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
Neural Processing Letters
Supervised learning in multilayer spiking neural networks
Neural Computation
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This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train firing rates and behave in a similar manner as fuzzy membership functions. The connectivity of the hidden and output layers in the fuzzy spiking neural network (FSNN) is representative of a fuzzy rule base. Fuzzy C-Means clustering is utilised to produce clusters that represent the antecedent part of the fuzzy rule base that aid classification of the feature data. Suitable cluster widths are determined using two strategies; subjective thresholding and evolutionary thresholding respectively. The former technique typically results in compact solutions in terms of the number of neurons, and is shown to be particularly suited to small data sets. In the latter technique a pool of cluster candidates is generated using Fuzzy C-Means clustering and then a genetic algorithm is employed to select the most suitable clusters and to specify cluster widths. In both scenarios, the network is supervised but learning only occurs locally as in the biological case. The advantages and disadvantages of the network topology for the Fisher Iris and Wisconsin Breast Cancer benchmark classification tasks are demonstrated and directions of current and future work are discussed.