Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

Abstract

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.

Fast Learning with Weak Synaptic Plasticity

“…2B). This is the result of the STDP mechanism we chose, which produces bimodal distributions of weights (van Rossum et al, 2000;Gütig et al, 2003), and this is consistent with the fact that neurons, in particular hippocampal cells, are known to have a large fraction of weak synapses (Isaac et al, 1995;Rumpel et al, 1998). Weak synapses do not contribute to the V m fluctuations, but they can, in principle, be potentiated (Fig.…”

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

“…In our simulations, we used Gütig et al, 2003), and considered ␣ to equal either 0 or 1. Note that for ␣ ϭ 0, the model reduces to the so-called additive STDP model (Song et al, 2000).…”

Networks that learn the precise timing of event sequences

“…(2) describes a Hebbian rate-based plasticity rule with soft bounds involving only linear and quadratic dependences of the pre-and postsynaptic rates (Gerstner and Kistler, 2002). Temporal asymmetry that accounts for the causal link between pre-and post-synaptic activity is incorporated with a small delay in the dependence of presynaptic activity u pre (t − D) (Gütig et al, 2003). This learning rule is a firing-rate version of the calcium-based plasticity model proposed by Graupner and Brunel (2012).…”