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Method development for co-simulation of electrical-chemical systems in Neuroscience
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0001-8678-910X
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Multiscale modeling and simulation is a powerful approach for studying such phenomena in nature as learning and memory. In computational neuroscience, historically, methods and tools for neuronal modeling and simulations have been developed for studies focused on a single level of the neuronal organization. Once the community realized that the interaction of multiple systems acting at different temporal and spatial scales can lead to emerging properties of the phenomena under study, the interest in and need for models encompassing processes acting at multiple scales of time and space increased. Such models are called multiscale models.

Multiscale modeling and simulation can be achieved in different ways. One of the possible solutions is to use an already existing foundation of formalisms and methods, and couple existing numerical algorithms and models during a simulation in a co-simulation, i.e. a joint simulation of subsystems. However, there are several obstacles on the way. First, a lack of interoperability of simulation environments makes it non-trivial to couple existing models in a single environment that supports multiscale simulation. Second, there is a decision to make regarding which variables to communicate between subsystems. The communication signal has a significant impact on the behavior of the whole multiscale system. Last but not least, an absence of a theory or general approach for the numerical coupling of existing mathematical formalisms makes the coupling of the numerical solvers a challenging task.

The main contribution of this thesis is a numerical framework for multiscale co-simulation of electrical and chemical systems in neuroscience. A multiscale model that integrates a subcellular signaling system with the electrical activity of the neuron was developed. The thesis emphasizes the importance of numerically correct and efficient coupling of the systems of interest. Two coupling algorithms, named singlerate and multirate, differ in the rate of communication between the coupling subsystems, were proposed in the thesis. The algorithms, as well as test cases, were implemented in the MATLAB® environment. MATLAB was used to validate the accuracy and efficiency of the algorithms. Both algorithms showed an expected second order accuracy with the simulated electrical-chemical system. The guaranteed accuracy in the singlerate algorithm can be used as a trade-off for efficiency in the multirate algorithm. Thus, both algorithms can find its application in the proposed numerical framework for multiscale co-simulations. The framework exposes a modular organization with natural interfaces and could be used as a basis for the development of a generic tool for multiscale co-simulations.

The thesis also presents an implementation of a new numerical method in the NEURON simulation environment, with benchmarks. The method can replace the standard discretization schema for the Hodgkin-Huxley type models. It can be beneficial in a co-simulation of large models where the Jacobian evaluation of the whole system becomes a very expensive operation.

Finally, the thesis describes an extension of the MUlti-SImulation Coordinator tool (MUSIC). MUSIC is a library that is mainly used for co-simulations of spiking neural networks on a cluster. A series of important developments was done in MUSIC as the first step towards multiscale co-simulations. First, a new algorithm and an improvement of the existing parallel communication algorithms were implemented as described in the thesis. Then, a new communication scheduling algorithm was developed and implemented in the MUSIC library and analyzed. The numerical framework presented in the thesis could be implemented with MUSIC to allow efficient co-simulations of electrical-chemical systems.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2020.
Series
TRITA-EECS-AVL ; 2020:9
Keywords [en]
multiscale, multirate, co-simulation, electrical-chemical
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-266893ISBN: 978-91-7873-419-1 (print)OAI: oai:DiVA.org:kth-266893DiVA, id: diva2:1388512
Public defence
2020-02-14, Kollegiesalen, Brinellvägen 8, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20200127

Available from: 2020-01-27 Created: 2020-01-24 Last updated: 2020-03-09Bibliographically approved
List of papers
1. Efficient Spike Communication in the MUSIC Framework on a Blue Gene/Q Supercomputer
Open this publication in new window or tab >>Efficient Spike Communication in the MUSIC Framework on a Blue Gene/Q Supercomputer
(English)Manuscript (preprint) (Other (popular science, discussion, etc.))
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-266892 (URN)
Note

QC 20200127

Available from: 2020-01-24 Created: 2020-01-24 Last updated: 2020-02-06Bibliographically approved
2. Multirate method for co-simulation of electrical-chemical systems in multiscale modeling
Open this publication in new window or tab >>Multirate method for co-simulation of electrical-chemical systems in multiscale modeling
Show others...
2017 (English)In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 42, no 3, p. 245-256Article in journal (Refereed) Published
Abstract [en]

Multiscale modeling by means of co-simulation is a powerful tool to address many vital questions in neuroscience. It can for example be applied in the study of the process of learning and memory formation in the brain. At the same time the co-simulation technique makes it possible to take advantage of interoperability between existing tools and multi-physics models as well as distributed computing. However, the theoretical basis for multiscale modeling is not sufficiently understood. There is, for example, a need of efficient and accurate numerical methods for time integration. When time constants of model components are different by several orders of magnitude, individual dynamics and mathematical definitions of each component all together impose stability, accuracy and efficiency challenges for the time integrator. Following our numerical investigations in Brocke et al. (Frontiers in Computational Neuroscience, 10, 97, 2016), we present a new multirate algorithm that allows us to handle each component of a large system with a step size appropriate to its time scale. We take care of error estimates in a recursive manner allowing individual components to follow their discretization time course while keeping numerical error within acceptable bounds. The method is developed with an ultimate goal of minimizing the communication between the components. Thus it is especially suitable for co-simulations. Our preliminary results support our confidence that the multirate approach can be used in the class of problems we are interested in. We show that the dynamics ofa communication signal as well as an appropriate choice of the discretization order between system components may have a significant impact on the accuracy of the coupled simulation. Although, the ideas presented in the paper have only been tested on a single model, it is likely that they can be applied to other problems without loss of generality. We believe that this work may significantly contribute to the establishment of a firm theoretical basis and to the development of an efficient computational framework for multiscale modeling and simulations.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2017
Keywords
Adaptive time step integration, Backward differentiation formula, Co-simulation, Coupled integration, Coupled system, Multirate integration, Multiscale modeling, Multiscale simulation, Parallel numerical integration
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-207312 (URN)10.1007/s10827-017-0639-7 (DOI)000400077500003 ()28389716 (PubMedID)2-s2.0-85017136818 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 604102EU, Horizon 2020, 720270Swedish Research CouncilSwedish e‐Science Research CenterScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20170609

Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2020-03-09Bibliographically approved
3. Efficient Integration of Coupled Electrical-Chemical Systems in Multiscale Neuronal Simulations
Open this publication in new window or tab >>Efficient Integration of Coupled Electrical-Chemical Systems in Multiscale Neuronal Simulations
Show others...
2016 (English)In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 10, article id 97Article in journal (Refereed) Published
Abstract [en]

Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. First, the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. Then, the components can be defined by different mathematical formalisms. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, in neuroscience, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components comprising a multiscale test problem in neuroscience. We introduce an efficient coupling method based on the second-order backward differentiation formula (BDF2) numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used in neuroscience for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale neuroscientific test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA, 2016
Keywords
multiscale modeling, multiscale simulation, co-simulation, coupled system, adaptive time step integration, backward differentiation formula, parallel numerical integration, coupled integration
National Category
Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-193806 (URN)10.3389/fncom.2016.00097 (DOI)000383015600001 ()27672364 (PubMedID)2-s2.0-84989336945 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 604102Swedish Research CouncilSwedish e‐Science Research Center
Note

QC 20161024

Available from: 2016-10-24 Created: 2016-10-11 Last updated: 2020-01-24Bibliographically approved

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