A key feature of neural circuits is the presence of divergence and convergence: A single neurone receives inputs from hundreds or even thousands of other neurones, and the sequence of output spikes which this neurone generates will, in turn, affect many other neurones.
This raises the question: What characteristics of large scale synaptic
input determine the time of firing of an individual neurone?
Neural Modelling studies have been undertaken to investigate this issue
using The Common Input Model.
The Common Input Model
The common input model consists of two (often identical) neurones (A and B) which share a proportion of their synaptic inputs (Common). Each cell also has independent synaptic inputs (Ind). The two cells exhibit a tendency for correlated discharge, due entirely to the effects of the common inputs. The Common and Independent inputs can consist of many hundreds or thousands of individual synaptic inputs. The common input configuration provides more opportunities for exploring neural integration than a single cell configuration:


The Common Input Model, Large Scale Synaptic Input and Neural Bandwidth.
Results from this work have provided new insight into the functional
role of correlated neuronal activity, suggesting an important role for
weak stochastic temporal correlation amongst presynaptic inputs in determining
the output firing times, and leading to the concept of Dynamic Modulation
of Neural Bandwidth
References to recent neural modelling work: are in the
publications page
Next: Dynamic Modulation of Neural Bandwidth