  Synaptic integration is a prominent aspect of neuronal information
processing. The detailed mechanisms that modulate synaptic inputs determine the
computational properties of any given neuron. We study a simple model for the
summation of excitatory inputs from synapses and illustrate its use by
characterizing some functional properties of postsynaptic neurons. In this
regard, we study the response of postsynaptic neurons as defined by the model
to two well known noise driven processes: stochastic and coherence resonance.
The model requires a small number of parameters and is especially useful to
isolate the role of integration mechanisms that rely on summation of inputs
with little dendritic processing.
