A modelling study on discrimination tasks.

We investigate the discrimination capacity of a single neuron model including the integrate-and-fire (IF) model and the IF-FHN model [Neural Networks, 14 (2001) 955], both theoretically and numerically. Both magnitude and frequency discrimination tasks are considered. It is found that for the magnitude discrimination task, with a small fraction of coherent inputs, an IF model and an IF-FHN model with inhibitory inputs can tell one input from the other. The total probability of misclassifications (TPM) is considerably reduced with the increasing of inhibitory inputs. However, the IF model and the IF-FHN model are incapable of performing the frequency discrimination task.