Long-term potentiation (LTP) has long been held as a biological substrate for associative learning. Recently, evidence has emerged that long-term depression (LTD) results when the presynaptic cell fires after the postsynaptic cell. The computational utility of LTD is explored here. Synaptic modification kernels for both LTP and LTD have been proposed by other laboratories based studies of one postsynaptic unit. Here, the interaction between time-dependent LTP and LTD is studied in small networks.