BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

Giuseppe Chindemi, Jean-Denis Georges Emile Courcol, Michael Emiel Gevaert, Henry Markram, Eilif Benjamin Muller, Idan Segev, Werner Alfons Hilda Van Geit
Frontiers Research Foundation, 2016
Article

Résumé

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parametrising such models to conform to the multitude of available experimental constraints is a global nonlinear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases

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The ability of simple mathematical models to predict the activity of single neurons is important for computational neuroscience. In neurons, stimulated by a time-dependent current or conductance, we want to predict precisely the timing of spikes and the sub-threshold voltage. During the last years several models have been tested on this type of data but never compared with the same protocol. One of the major outcome is that, from a certain degree of complexity, all are very efficient and gave statistically indistinguishable results. We studied a class of integrate-and-fire models (IF), with each member of the class implementing a selection of possible improvements: exponential voltage non-linearity1, spike-triggered adaptation current2, spike-triggered change in conductance, moving threshold3, sub-threshold voltage-dependent currents4. Each refinement adds a new term to the equations of the IF model. This IF family is extendable and adaptable to different neuron types and is able to deal with complex neural activities (i.e. adaptation, facilitation, bursting, relative refractoriness, ...). To systematically explore the effects of a given term of the model a new fitting procedure based on linear regression of voltage change5 is used. This method is fast, robust and allows the extraction of all the models parameters from a few seconds recordings of fluctuating injected current and membrane potential with hundreds spikes without any prior knowledge. To investigate the effect of our modifications, we used as training data three different Hodgkin-Huxley-like models, and two experimental recordings of fast spiking and regular spiking cells and then evaluate each IF model on a given test set. We observe that it is possible to fit a model that can reproduce the activity of neurons with high reliability (i.e. almost 100 % of the spike time and less than 1 mV of sub-threshold voltage difference). Using this framework one can classify IF models in terms of complexity and performance and evaluate the importance of each term for different stimulation paradigms.

2010

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Viability evolutionary algorithms and applications to neuroscience and biology

Andrea Maesani

Evolutionary algorithms are heuristic methods that operate on a population of individuals, which represent candidate solutions to optimization problems. Individuals with better quality, or fitness, are reproduced at a higher rate than less fit individuals by recombination and mutation. This process of selection and reproduction, based on fitness, models competition between individuals and leads to survival-of-the-fittest. Although this process lays at the foundation of evolutionary algorithms, the simplistic assumption that a single criterion can describe the quality of individuals is often unmet in real world problems, where multiple objectives or constraints have to be combined in the same fitness function. Furthermore, in nature, survival-of-the-fittest does not derive uniquely from competition, but is better seen as the outcome of an ongoing process of competition in parallel to a process of elimination of non-fit individuals. To resolve this mismatch and model different criteria for individuals separately, the Viability Evolution paradigm has been proposed in the literature. While the idea was previously introduced at a conceptual level, it had not yet been put to the test and lacks experimental validation. Here, we bridged this gap and tested whether viability-inspired evolutionary algorithms could confer advantages over the traditional evolutionary computation paradigm based on fitness. We started by investigating the evolutionary dynamics of a simple viability evolutionary method, and we analyzed the diversity of evolving populations. We also showed that the additional information available to a viability-based algorithm, such as the number of non-viable individuals for each viability criteria, can be exploited during the search for achieving better performance on optimization problems. In particular, we devised viability algorithms for constrained optimization, which outperformed current state-of-the-art methods on a well-known set of constrained optimization benchmarks. Furthermore, we embedded one of our algorithms into a novel framework for predicting structures of protein assemblies at atomic resolution. Finally, we used evolutionary methods to optimize neural networks in the context of a neuroscience investigation on the role of noise in animal behaviors. Noise is a ubiquitous feature of neural systems, but how it affects behaviors is far from being fully understood. After collecting large-scale measurements of Drosophila walking, we used evolutionary algorithms to search for neural networks that could match observed patterns of Drosophila spontaneous and odor-induced walking. By combining dynamical systems analysis and simulations, we showed how a noise-mediated memory of odor exposure accounts for the observed responses. In conclusion, this thesis contributes to evolutionary computation by testing the Viability Evolution paradigm and by proposing efficient viability evolutionary algorithms for constrained optimization. Furthermore, this thesis contributes to biology, by providing a new viability-based framework for predicting protein assemblies and by elucidating a mechanism through which neural noise may shape animal behaviors.

EPFL2015

Chargement

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008

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Simple Neuron Models and Adaptation

Skander Mensi

2008

Viability evolutionary algorithms and applications to neuroscience and biology

Andrea Maesani

EPFL2015

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2010