Abstract
Computational modeling of brain circuits requires the definition of many parameters that are difficult to determine from experimental findings. One way to help interpret these data is to fit them using a particular kinetic model. In this paper, we propose a general procedure to fit individual synaptic events recorded from voltage clamp experiments. Starting from any given model description (mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. The procedure, implemented in NEURON, is currently available in ModelDB. A Python version is installed, and will be soon available for public use, as a standalone task in the Collaboratory Portal of the Human Brain Project. To illustrate the potential application of the procedure, we tested its use with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. For individual spontaneous inhibitory events in hippocampal pyramidal CA1 neurons, we found that gephyrin-dependent subcellular pathways may shape synaptic events at different levels, and can be correlated with cell- or event-specific activity history and/or pathological conditions.
Highlights
The computational modeling of brain circuits, at practically any integration level, requires many parameters to be defined that, ideally, should be experimentally determined or constrained by experimental data or findings
We obtained a total of 4712 raw experimental spontaneous inhibitory post-synaptic currents (sIPSCs) recordings to use in our fitting procedure
The procedure will be available in ModelDB, implemented using the NEURON simulation environment (Hines and Carnevale, 1997) and its built-in PRAXIS fitter
Summary
The computational modeling of brain circuits, at practically any integration level, requires many parameters to be defined that, ideally, should be experimentally determined or constrained by experimental data or findings. If a researcher is interested in testing a specific kinetic scheme implemented for specific biochemical pathways, the use of individual events is the most appropriate choice, since this approach would give information on the different combinations of model parameters that are consistent with the observed events. Experimental data, kinetic models of synaptic transmission, and fitting parameters and their dependencies can be user defined/provided or gathered from databases. They can be used to generate optimized groups of parameters able to represent a population of synapses, either for simulation purposes or to study the functional consequences of a particular protein or subcellular synaptic transmission pathway. Data were arbitrarily divided in 5 different experiments (expA-E)
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