The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences
Figure 3
Most of the commonly used rate-based learning rules do not provide a positive correlation between weight and postsynaptic activity.
The slope of the pd-term in Equation 2 is determined by the slope of the fixed weight depending on postsynaptic activity ( relation) resulting from the synaptic plasticity rule. Here we show the relation of commonly used learning rules [18] for the simple feedforward system (top row) and for a linear approximation of a feedback system, where the presynaptic activity equals the postsynaptic activity (bottom row). For reproducing experimental data, pd has to grow, i.e. the fixed weights have to increase with postsynaptic activity. This is fulfilled (red shaded area), for example, by the BCM-learning rule with synaptic scaling or a Hebb-like learning rule with synaptic scaling and feedback but also for more biological rules like the calcium-based plasticity rule from [24] (see Materials and Methods for parameters used to generate this figure).