Abstract
Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. 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. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. 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. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies.
Highlights
Neurons have long been interpreted as passive integrators of input signals that fire action potentials when a threshold is reached (Knight, 1972)
T2N can be used to create robust models for any neuron type, in this work we focused on hippocampal dentate granule cells, which play a crucial role in learning and memory and exhibit the unique feature that they integrate into the adult hippocampal network as newborn neurons throughout life
Since the TREES toolbox (Cuntz et al, 2010, 2011) is a recently established versatile tool for the analysis and modeling of 3D morphologies of dendrites, its coupling to NEURON (Carnevale and Hines, 2006) opens many new possibilities: (1) Biophysical mechanisms can be inserted into reconstructed and into synthetic morphologies, which is important for the creation of a large set of realistic compartmental models capturing neuron-to-neuron variability of dendritic trees
Summary
Neurons have long been interpreted as passive integrators of input signals that fire action potentials when a threshold is reached (Knight, 1972) This paradigm has changed as the output of neurons was shown to depend on many intrinsic cellular mechanisms (e.g. voltagegated channels, dendritic architecture, synaptic plasticity, active dendrites, axon initial segment) indicating that single neuron computation is rather complex (Softky and Koch, 1993; Brunel et al, 2014; Volgushev, 2016). Many recent models include reconstructed morphologies, which are often available online through specialized databases As these models seem to become more and more realistic, the hope arises that one will soon be able to simulate entire circuits or even the brain itself by including more and more details (Markram, 2006, 2012; Markram et al, 2015; Hawrylycz et al, 2016).
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