An Introductory example

In this section we will introduce LsmTool by means of an introductory example. In this simple example we will train a readout neuron modeled as a threshold gate (see [Hertz et al., 1991]) to classify a spike train. This readout neuron will receive its input from a neural microcircuit modeled as a network of leaky-integrate-and-fire neurons (see [Gerstner and Kistler, 2002]) which is stimulated by the input spike train (which should be classified). The setup is shown in Figure 1.

Figure: Architecture used to classify a spike train. The microcircuit is modeled as a network of leaky-integrate-and-fire neurons.
\includegraphics{fig_example}

Precise definition of the classification task

Two Poisson spike trains (freqency 20Hz, length 0.5sec) are generated, and fixed as templates 0 and 1. The actual input spike train is generated as jittered versions of a template by varying each spike by a random drawn amount (Gaussion distribuion with zero mean and a given STD; this STD is called jitter (default jitter=4ms)). The task of the threshold gate is to output the number (0 or 1) of the (random choosen) template from which the input spike train was generated.