NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Note on learning rate schedules for stochastic optimization
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Dynamics and algorithms for stochastic search
Dynamics and algorithms for stochastic search
Two approaches to optimal annealing
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Exact and perturbation solutions for the ensemble dynamics
On-line learning in neural networks
Weight Space Probability Densities in Stochastic Learning: II. Transients and Basin Hopping Times
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Neural Computation
Phenomenological models of synaptic plasticity based on spike timing
Biological Cybernetics - Special Issue: Object Localization
Systematic fluctuation expansion for neural network activity equations
Neural Computation
Hi-index | 0.00 |
Online machine learning rules and many biological spike-timing-dependent plasticity (STDP) learning rules generate jump process Markov chains for the synaptic weights. We give a perturbation expansion for the dynamics that, unlike the usual approximation by a Fokker-Planck equation (FPE), is well justified. Our approach extends the related system size expansion by giving an expansion for the probability density as well as its moments. We apply the approach to two observed STDP learning rules and show that in regimes where the FPE breaks down, the new perturbation expansion agrees well with Monte Carlo simulations. The methods are also applicable to the dynamics of stochastic neural activity. Like previous ensemble analyses of STDP, we focus on equilibrium solutions, although the methods can in principle be applied to transients as well.