A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data

By Shaul Druckmann, Y. Banitt, A. Gidon, Felix Schürmann, Henry Markram, Idan Segev. Frontiers in Neuroscience, 2007

319 citations recorded by the source corpus.

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4d926d6ba26507746fa08b77a44d589cda78b78c

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

How does Nature Program Neuron Types? (2008, methodology): “When thinking about it in programming terms, this would be identical to parameter fitting in a model simulation: the task is to find the minimum of the error function between the desired output (the ‘target’) and the actual output of the model (e.g. Borst and Haag, 1996; Druckmann et al, 2007).”
Flexible statistical inference for mechanistic models of neural dynamics (2017, methodology): “Biophysical neuron models are typically fit to data with genetic algorithms applied to the distance between simulated and measured data-features [2, 8, 9, 46].”
Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties (2011, methodology): “In this work we extended our theoretical framework”
Likelihood-free inference with emulator networks (2018, methodology): “Fitting single-and multi-compartment Hodgkin-Huxley models to neurophysiological data is a central problem in neuroscience, and typically addressed using non-Bayesian approaches based on evolutionary optimization [33, 34].”
Reconstruction and Simulation of Neocortical Microcircuitry. (2015, methodology): “Neuron models were fitted using a feature-based multi-objective optimization method, as previously described (Druckmann et al., 2007).”