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Algorithm for offline training of the threshold gate

Here we just outline the main body of the procedure which we will use to train the readout. This will be discussed in more detail in Section .

1. Define the neural microcircuit to be analyzed

2. Record spike responses of the neural microcircuit caused by different training inputs drawn from an appropriate input distribution.

3. Convert the spike responses into states x(t_k) at various sample time points t_k by some low-pass filtering to get a somewhat smoothed signal. This mimics the effect of spike transmission through a synapse to its postsynaptic neuron. This transformation can also be dropped if one can cope directly with the spike response.

4. Apply a supervised learning algorithm to a set of training examples of the form $\left<\right.$state x, target-value  $\left.y\right>$ to train a readout function f (a threshold gate in the case of this example) such that the actual outputs f(x) are as close as possible to the target values y given by the target function.

5. Evaluate the performance of the trained readout (i.e. the threshold gate) on an independent set of test inputs (which are usually drawn from the same distribution as the training inputs).

(C) 2003, Thomas Natschläger

last modified 06/12/2006