## Intrinsic Stabilization of Output Rates by Spike-Time Dependent Hebbian Learning (1999)

Venue: | Neural Computation |

Citations: | 3 - 0 self |

### BibTeX

@ARTICLE{Kempter99intrinsicstabilization,

author = {Richard Kempter and Wulfram Gerstner and J. Leo Van Hemmen},

title = {Intrinsic Stabilization of Output Rates by Spike-Time Dependent Hebbian Learning},

journal = {Neural Computation},

year = {1999},

volume = {13},

pages = {274--2}

}

### OpenURL

### Abstract

Over a broad parameter regime, spike-time dependent learning leads 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 are identical at all synapses and correlations in the input are translation invariant. In a rate description, stabilization of the postsynaptic firing rate is most easily achieved by a negative correlation term in the learning rule, often called `anti-Hebbian' learning. For spike-based learning, a strict distinction between Hebbian and `anti-Hebbian' rules is no longer possible. Specifically, learning is driven by correlations on the time scale of the learning window which may be positive even though the integral over the learning window is negative. While the negative integral leads to intrinsic rate stabilization, the positive part of the learning window picks up temporal...