![]() ![]() This paradigm has meanwhile changed as the output of neurons was shown to depend on many intrinsic cellular mechanisms (e.g. ![]() ![]() Traditionally, neurons have long been interpreted as passive integrators of input signals that fire action potentials when a threshold is reached ( Knight, 1972). We discuss possible T2N application in degeneracy studies. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. This work sets a new benchmark for detailed compartmental modeling. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Compartmental models are the theoretical tool of choice for understanding single neuron computations. ![]()
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