Parabolic bursting in an excitable system coupled with a slow oscillation
SIAM Journal on Applied Mathematics
Natural gradient works efficiently in learning
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Firing rate of the noisy quadratic integrate-and-fire neuron
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Self-Organization of Spiking Neural Network Generating Autonomous Behavior in a Real Mobile Robot
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Advances in Design and Application of Spiking Neural Networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
Finding iterative roots with a spiking neural network
Information Processing Letters - Special issue on applications of spiking neural networks
A gradient descent rule for spiking neurons emitting multiple spikes
Information Processing Letters - Special issue on applications of spiking neural networks
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Event-driven simulations of nonlinear integrate-and-fire neurons
Neural Computation
Type i membranes, phase resetting curves, and synchrony
Neural Computation
Lower bounds for the computational power of networks of spiking neurons
Neural Computation
Regularized nonsmooth Newton method for multi-class support vector machines
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
Adaptive synchronization of activities in a recurrent network
Neural Computation
Error-backpropagation in networks of fractionally predictive spiking neurons
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Supervised learning in multilayer spiking neural networks
Neural Computation
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The main contribution of this letter is the derivation of a steepest gradient descent learning rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model. Central to our model is the assumption that the intrinsic neuron dynamics are sufficient to achieve consistent time coding, with no need to involve the precise shape of postsynaptic currents; this assumption departs from other related models such as SpikeProp and Tempotron learning. Our results clearly show that it is possible to perform complex computations by applying supervised learning techniques to the spike times and time response properties of nonlinear integrate and fire neurons. Networks trained with our multilayer training rule are shown to have similar generalization abilities for spike latency pattern classification as Tempotron learning. The rule is also able to train networks to perform complex regression tasks that neither SpikeProp or Tempotron learning appears to be capable of.