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The Journal of Neuroscience, May 1, 2003, 23(9):3697
Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity
R. Gütig1, *, R. Aharonov2, *, S. Rotter1, and Haim Sompolinsky2, 3 1 Institute of Biology III, University of Freiburg, 79104 Freiburg, Germany, and 2 Interdisciplinary Center for Neural Computation and 3 Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel
Triggered by recent experimental results, temporally asymmetric Hebbian (TAH) plasticity is considered as a candidate model for the biological implementation of competitive synaptic learning, a key concept for the experience-based development of cortical circuitry. However, because of the well known positive feedback instability of correlation-based plasticity, the stability of the resulting learning process has remained a central problem. Plagued by either a runaway of the synaptic efficacies or a greatly reduced sensitivity to input correlations, the learning performance of current models is limited. Here we introduce a novel generalized nonlinear TAH learning rule that allows a balance between stability and sensitivity of learning. Using this rule, we study the capacity of the system to learn patterns of correlations between afferent spike trains. Specifically, we address the question of under which conditions learning induces spontaneous symmetry breaking and leads to inhomogeneous synaptic distributions that capture the structure of the input correlations. To study the efficiency of learning temporal relationships between afferent spike trains through TAH plasticity, we introduce a novel sensitivity measure that quantifies the amount of information about the correlation structure in the input, a learning rule capable of storing in the synaptic weights. We demonstrate that by adjusting the weight dependence of the synaptic changes in TAH plasticity, it is possible to enhance the synaptic representation of temporal input correlations while maintaining the system in a stable learning regime. Indeed, for a given distribution of inputs, the learning efficiency can be optimized.
Key words: Hebbian learning; spike-timing-dependent plasticity; synaptic updating; symmetry breaking; unsupervised learning; infomax; activity-dependent development
* R.G. and R.A. contributed equally to this work.
Copyright © 2003 Society for Neuroscience 0270-6474/03/2393697-18$05.00/0
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