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The resulting integration boundary is then the k th spike time of the μ th spike train.
Despite the drift, over shorter numbers of trials, the mean spike time of each neuron has high accuracy (Figure 9D).
The red arrow denotes the average pop spike time of all included experiments (4.36 ± 0.19 ms).
We then generated sets of randomized data by taking the actual data and jittering every spike time of only preconnection neurons [neuron i in Equation (2)] by an amount randomly drawn from a uniform distribution of a window size T.
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In such a pattern, all the spike times of all the neurons are exactly predefined.
The stochastic character is embedded in the distribution of the spike times of neurons.
Next, we introduce the model for a population of spiking neurons where independent noise shifts the spike times of the output spike trains.
It can choose to add a neuron, update the network parameters or delete a spike pattern depending on the spike times of the output neurons.
In another case, we assume that independent noise shifts the spike times of the output spike trains (STS model) as illustrated in Fig. 2b.
In other words, one can know the value of the conductances g k j at time t only if the past spike times of the network are known.
Based on the weight sensitivity modulation factor and the postsynaptic potential of a neuron, the meta-neuron based learning rule updates the synaptic weights in the network to produce precise shifts in the spike times of the postsynaptic neurons.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com