This neural network has been done with the Animatlab software using only firing realistic neurons.
Here, to simplify, there is only one input neuron, in red. But the principle is the same with several input neurons. Each input neuron has to project to the delay line neurons, in orange. The last neuron of the delay line, in orange, has to project to the output neuron with a conduction weight depending on the number of spikes in the pattern to be recognize.
With this specific connectivity, the input neuron creates 3 links to the conduction delay neurons, hence the pattern to be recognize will contain 3 spikes. I have used a conduction delay of 10ms. This network will then recognize a spike pattern of 3 spikes with the following arrival time t1=0, t2=30ms and t3=40ms.
Everytime a spike input the delay line neural network, it creates a particular composite EPSP in the output neuron.
This particular shape of the membrane potential is created by the propagation times the different spikes take to go through the conduction line neural network. Because the propagation time are predictable as well as the axonic connections, the composite EPSP in the output neuron is hence predictable.
temporal decoding : precise spike sequence detection.
when the input have the particular timing of the network: t1=0, t2=30ms and t3=40ms, the output neuron recognize the sequence and fire.
the precise spike timing is also important even in the case of 3 input spikes.
if the timing is not good, the composite EPSPs do not combine well in the output neuron that cannot reach the membrane potential threshold to fire.
The precision of the timing can be modulated by tuning the decay time of each EPSP. With a longer duration of the EPSP it is possible to be less exigent on the precision of the timing.
We have seen here that with a conduction line neural network and only realistic neurons it is possible to make a temporal decoder: the output neuron can fire only when a precise spatio-temporal spike pattern is in input.