Construction of low-order computational models which accommodate spatial distributions of afferent synapses received by thalamocortical neurons.
Mike Neubig and Alain Destexhe

Society for Neuroscience Abstracts 23: 573, 1997.

Low-order computational models that accommodate the various spatial distributions of afferent synapses deriving from the retinal and brainstem ascending pathways and from the corticothalamic feedback pathway, are presented for thalamocortical neurons.

Morphological and biophysical parameter space is examined. Physiologic parameter basins are determined such that low-order model behavior is congruent with experiment. Two-dimensional slices of the global physiologic basin are presented.

Metrics are devised that quantify critical behaviors. These metrics are applied to in-vivo/in-vitro data as well as simulation data from the model, and morphological and biophysical parameter values are obtained constrained by minimizing the difference.

This method of model formulation – constraining morphological and biophysical parameters by minimizing behavioral metrics – may be applied to any neuron and offers an alternative to the method of algorithmic reduction of dendritic arbors. Comparisons are made in this regard.

Computational efficiency of low-order models make them suitable for network level investigations. The particular models presented in this case study are suitable for investigations of the dynamics of prototypical thalamic circuits.

Supported by FRSQ and MRC of Canada.