Abstract
The construction of compartmental models of neurons involves tuning a set of parameters to make the model neuron behave as realistically as possible. While the parameter space of single-compartment models or other simple models can be exhaustively searched, the introduction of dendritic geometry causes the number of parameters to balloon. As parameter tuning is a daunting and time-consuming task when performed manually, reliable methods for automatically optimizing compartmental models are desperately needed, as only optimized models can capture the behavior of real neurons. Here we present a three-step strategy to automatically build reduced models of layer 5 pyramidal neurons that closely reproduce experimental data. First, we reduce the pattern of dendritic branches of a detailed model to a set of equivalent primary dendrites. Second, the ion channel densities are estimated using a multi-objective optimization strategy to fit the voltage trace recorded under two conditions – with and without the apical dendrite occluded by pinching. Finally, we tune dendritic calcium channel parameters to model the initiation of dendritic calcium spikes and the coupling between soma and dendrite. More generally, this new method can be applied to construct families of models of different neuron types, with applications ranging from the study of information processing in single neurons to realistic simulations of large-scale network dynamics.
Highlights
To incorporate realism into large-scale simulations of cortical and other networks (Traub et al, 2005; Markram, 2006), one needs to construct biophysically realistic compartmental models of the individual neurons in the circuit
We construct the reduced pyramidal cell models step by step, applying methods adapted to the problem (Roth and Bahl, 2009)
We optimized the dendritic calcium channel parameters and adjusted these values such that the model reproduces the shape of the dendritic calcium AP as well as somato-dendritic coupling factors found in experiments (Schaefer et al, 2003). After pursuing these three steps in optimizing neuronal models, we present a family of 10 reduced models of layer 5 pyramidal neurons whose input–output relation matches a range of experimental data
Summary
To incorporate realism into large-scale simulations of cortical and other networks (Traub et al, 2005; Markram, 2006), one needs to construct biophysically realistic compartmental models of the individual neurons in the circuit. We apply a powerful optimization strategy, Evolutionary Multi-Objective Optimization (EMOO) (Deb et al, 2002; Druckmann et al, 2007) This method, starting from a family of models characterized by multiple features, generates a suite of new models at each step, without making an a priori determination as to which one of the multiple desired objectives is most important
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