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Abstract:
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.
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