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.
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