Learning, Other Methods¶
Linear Homeostatic Plasticity¶
Formulation¶
\[
v(t + \Delta t) - v(t) = \lambda \frac{r^* - r}{r^*}
\]
Where:
\(v\), an updatable parameter
\(r^*\), target spike rate
\(r\), observed spike rate
\(\lambda\), learning rate
\(t\), current simulation time
\(\Delta t\), duration of the simulation step
Description¶
This method is used for regulating the trainable parameters of a network trained with another plasticity method based on a target spiking rate. As originally provided, weight updates use a positive value for \(\lambda\) and delay updates use a negative value for \(\lambda\).