Multiplying with neurons: compensation for irregular input spike trains by using time-dependent synaptic efficiencies

A leaky integrate-and-fire (LIF) neurons can act as multipliers by detecting coincidences of input spikes. However, in case of input spike trains with irregular interspike delays, false coincidences are also detected and the operation as a multiplier is degraded. This problem can be solved by using time dependent synaptic weights which are set to zero after each input spike and recover with the same time constant as the decay time of the corresponding excitatory postsynaptic potentials (EPSP). Such a mechanism results in EPSP's with amplitudes independent on the input interspike delays. Neuronal computation is then performed without frequency decoding.