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: A correlation analysis of the two output discharges is the same for 1 or 1000 common inputs. The strength and characteristics of correlation in the common synaptic inputs can be compared with that in the outputs. It often occurs in an experimental situation: Multiple outputs can be recorded, access to the inputs is not possible. The single cell configuration is available as part of this 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 pre-synaptic 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

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