Spike-Based Compared to Rate-Based Hebbian Learning
Abstract
A correlation-based learning rule at the spike level is formulated, mathematically analyzed, and compared to learning in a firing-rate description. A differential equation for the learning dynamics is derived under the assumption that the time scales of learning and spiking can be separated. For a linear Poissonian neuron model which receives time-dependent stochastic input we show that spike correlations on a millisecond time scale play indeed a role. Corre(cid:173) lations between input and output spikes tend to stabilize structure formation, provided that the form of the learning window is in accordance with Hebb's principle. Conditions for an intrinsic nor(cid:173) malization of the average synaptic weight are discussed.
Cite
Text
Kempter et al. "Spike-Based Compared to Rate-Based Hebbian Learning." Neural Information Processing Systems, 1998.Markdown
[Kempter et al. "Spike-Based Compared to Rate-Based Hebbian Learning." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/kempter1998neurips-spikebased/)BibTeX
@inproceedings{kempter1998neurips-spikebased,
title = {{Spike-Based Compared to Rate-Based Hebbian Learning}},
author = {Kempter, Richard and Gerstner, Wulfram and van Hemmen, J. Leo},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {125-131},
url = {https://mlanthology.org/neurips/1998/kempter1998neurips-spikebased/}
}