Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to the theory of neural computation
Introduction to the theory of neural computation
The role of constraints in Hebbian learning
Neural Computation
What matters in neuronal locking?
Neural Computation
Hebbian learning of pulse timing in the Barn Owl auditory system
Pulsed neural networks
A developmental learing rule for coincidence tuning in the barn owl auditory system
CNS '96 Proceedings of the annual conference on Computational neuroscience : trends in research, 1997: trends in research, 1997
The handbook of brain theory and neural networks
Spike-based compared to rate-based Hebbian learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Encoding and Decoding of Patterns which are Correlated in Space and Time
Konnektionismus in Artificial Intelligence und Kognitionsforschung. Proceedings 6. Österreichische Artificial Intelligence-Tagung (KONNAI)
Unsupervised Learning in Networks of Spiking Neurons Using Temporal Coding
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates
Neural Computation
Neural Computation
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
Spike-Timing-Dependent Plasticity in Balanced Random Networks
Neural Computation
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Spiking neurons that keep the rhythm
Journal of Computational Neuroscience
Phase precession and recession with STDP and Anti-STDP
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Classification of distorted patterns by feed-forward spiking neural networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Hi-index | 0.00 |
We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time differences between presynaptic and postsynaptic spikes. The changes due to correlated input and output spikes are quantified by means of a learning window. We show that plasticity can lead to an intrinsic stabilization of the mean firing rate of the postsynaptic neuron. Subtractive normalization of the synaptic weights (summed over all presynaptic inputs converging on a postsynaptic neuron) follows if, in addition, the mean input rates and the mean input correlations are identical at all synapses. If the integral over the learning window is positive, firing-rate stabilization requires a non-Hebbian component, whereas such a component is not needed if the integral of the learning window is negative. A negative integral corresponds to anti-Hebbian learning in a model with slowly varying firing rates. For spike-based learning, a strict distinction between Hebbian and anti-Hebbian rules is questionable since learning is driven by correlations on the timescale of the learning window. The correlations between presynaptic and postsynaptic firing are evaluated for a piecewise-linear Poisson model and for a noisy spiking neuron model with refractoriness. While a negative integral over the learning window leads to intrinsic rate stabilization, the positive part of the learning window picks up spatial and temporal correlations in the input.