Forward Updates Versus Backward Updates

In all definitions of $\ TD(\gamma)$ we looks on updates occurring in forward direction in time (looking to the "future") like in MC method. We want to define scheme for online updates and so we need to find the way to look on updates in backward direction ("past"). We like that at the end of the run, updates would be the same for both schemes.
For implementation of such method, an additional variable which holds weight of a state in a run will be provided for every run. Every step we multiply all those variables (called eligibility trace) by $\ \gamma\lambda$ and for current state we add 1. Formally,
$\ e_{t}(s) = \gamma\lambda e_{t-1}(s)$ if $\ s\neq s_{t}$
$\ e_{t}(s) = \gamma\lambda e_{t-1}(s)+1$ if $\ s = s_{t}$
Every time we visit a state , its weight is going up and for states not visited theirs weight is going down. Let us define: $\ \delta_{t} = r_{t+1} + \lambda V_{t}(s_{t+1}) - V_{t}(s_{t})$.
Now the update would be, $\ \Delta V_{t}(s) = \alpha \delta_{t} e_{t}(s)$.