Evaluation of simplified compartmental models of reconstructed neocortical neurons for use in large-scale simulations of biological neural networks.
The electrotonic properties of the complex arborizations of neurons can be simulated by creating compartmental models based on the morphology of real neurons. These models can be very detailed with thousands of individual compartments and active channels. Large numbers of these models can be linked together into biologically realistic, large-scale neural networks with which to obtain a better understanding of the interactions among real neurons. However, the use of detailed compartmental models in such large networks is hindered by long computation times. Methods exist to reduce the complex morphology of detailed compartmental models to simpler reconstructions that retain many of the electrotonic properties of the original model yet are computationally efficient. However, little work exists that evaluates the limitations and performance of such reduced models with realistic active conductances modeled in both the soma and the dendrites to ensure that they are appropriate for use in biologically realistic network models. We have created detailed and reduced models of reconstructed dye-filled neurons from rat somatosensory neocortex and evaluated the ability of the reduced models to faithfully reproduce the input-output functions of the more detailed models. We find that the reduced models are not capable of perfectly reproducing the exact output of the detailed models using identical parameters. However, if the parameters are adjusted the reduced models are certainly capable of providing input-output patterns that are well within an acceptable range of known neural activity. The limitations and the benefits of such models are discussed.
Jackson
M E
ME
School of Human Development, Program in Neuroscience and Cognition, University of Texas at Dallas, Richardson 75803-0688, USA.
Cauller
L J
LJ
D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D005260:Female; D008297:Male; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D051381:Rats