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Other work focuses on mutual information in spike trains, measuring the transfer of information between input spikes and output spike response, with the goal of addressing reliability in rate coding [12].

This is surprising because the SIE relies only on the relationship between input spikes and output spikes.

Black arrows show the center of the input spike and red arrows show the depth where the difference between the initial and retrieved models is the maximum.

They found that the performance of a network with depressing synapses that discriminates between burst and random input spikes increases non-monotonically as the static device mismatch is increased.

In essence the SIE is the mutual information between an input spike train arriving at a synapse and the output spike train of the postsynaptic neuron.

A major advantage of this method is that it does not require any assumptions about correlations between spikes or the temporal relationship between input and output spikes.

We then used the sequence of times of the input sEPSC as an input "spike train" and computed the mutual information between this spike train and the output spike train emitted by the neuron to obtain the SIE for every current amplitude.

Based on the experimental results in this work, choosing optimized parameters for learning rate between 0.008 and 0.01, a frequency of input spikes 18 Hz, the sizes of mini-batches between 25 and 50, and two different membrane voltage thresholds for hidden and visible layers leads to the optimum results in the accuracy rate of the network recognition.

Hence, on average a NMDA-zone receives 75 input spike trains and 37.5 spike trains are shared between any two NMDA-zones.

Also concerning rate coding, in [14] a functional measure called the Synaptic Information Efficacy (SIE) is devised to measure mutual information between input and output spike trains in a noisy environment.

Before modeling the input spikes for any stimulus presentations, a template spike train is generated separately for each presynaptic neuron using equation 4. The actual input spike times for a single stimulus presentation are generated by jittering the spike times in each template with Gaussian noise of mean 0 and variance σ.