## Abstrakti

Multi-scale models in neuroscience typically integrate detailed biophysical

neurobiological phenomena from molecular level up to network and system levels. Such models are very challenging to simulate despite the availability of

massively parallel computing systems. Model Order Reduction (MOR) is an

established method in engineering sciences, such as control theory. MOR is used in improving computational efficiency of simulations of large-scale and complex

nonlinear mathematical models. In this study the dimension of a nonlinear

mathematical model of plasticity in the brain is reduced using mathematical MORmethods.

Traditionally, models are simplified by eliminating variables, such as

molecular entities and ionic currents, from the system. Additionally,

assumptions of the system behavior can be made, for example regarding the

steady state of the chemical reactions. However, the current trend in

neuroscience is incorporating multiple physical scales of the brain in

simulations. Comprehensive models with full system dynamics are needed in order to increase understanding of different mechanisms in one brain area. Thus the elimination approach is not suitable for the consequent analysis of neural

phenomena.

The loss of information typically induced by eliminating variables of the system

can be avoided by mathematical MOR methods that strive to approximate the

entire system with a smaller number of dimensions compared to the original

system. Here, the effectiveness of MOR in approximating the behavior of all

the variables in the original system by simulating a model with a radically

reduced dimension, is demonstrated.

In the present work, mathematical MOR is applied in the context of an experimentally verified signaling pathway model of plasticity (Kim et al., PLoS Comp. Biol.,

2013). This nonlinear chemical equation based model describes the biochemical

calcium signaling steps required for plasticity and learning in the subcortical

area of the brain. In addition to nonlinear characteristics, the model includes

time-dependent terms which pose an additional challenge both computational

efficiency and reduction wise.

The MOR method employed in this study is Proper Orthogonal Decomposition with

Discrete Empirical Interpolation Method (POD+DEIM), a subspace projection

method for reducing the dimensionality of nonlinear systems (Chaturantabut et

al., SIAM, 2010). By applying these methods, the simulation time of the model

is radically shortened. However, our preliminary studies show approximation error if the model is simulated for a very long time. The tolerated amount of approximation error depends on the final application of the model. Based on these promising results, POD+DEIM is recommended for dimensionality reduction in computational neuroscience.

In summary, the reduced order model consumes a considerably smaller amount of computational resources than the original model, while maintaining a low root

mean square error between the variables in the original and reduced models.

This was achieved by simulating the system dynamics in a lower dimensional

subspace without losing any of the variables from the model. The results presented here are novel as mathematical MOR has not been studied in neuroscience without linearisation of the mathematical model and never in the context of the model presented here.

1. Kim, B., Hawes, S.L., Gillani, F., Wallace, L.J. and Blackwell, K.T., 2013.

Signaling pathways involved in striatal synaptic plasticity are sensitive to

temporal pattern and exhibit spatial specificity. PLoS computational

biology, 9(3), p.e1002953.

2. Chaturantabut, S. and Sorensen, D.C., 2010. Nonlinear model reduction via

discrete empirical interpolation. SIAM Journal on Scientific Computing,

32(5), pp.2737-2764

neurobiological phenomena from molecular level up to network and system levels. Such models are very challenging to simulate despite the availability of

massively parallel computing systems. Model Order Reduction (MOR) is an

established method in engineering sciences, such as control theory. MOR is used in improving computational efficiency of simulations of large-scale and complex

nonlinear mathematical models. In this study the dimension of a nonlinear

mathematical model of plasticity in the brain is reduced using mathematical MORmethods.

Traditionally, models are simplified by eliminating variables, such as

molecular entities and ionic currents, from the system. Additionally,

assumptions of the system behavior can be made, for example regarding the

steady state of the chemical reactions. However, the current trend in

neuroscience is incorporating multiple physical scales of the brain in

simulations. Comprehensive models with full system dynamics are needed in order to increase understanding of different mechanisms in one brain area. Thus the elimination approach is not suitable for the consequent analysis of neural

phenomena.

The loss of information typically induced by eliminating variables of the system

can be avoided by mathematical MOR methods that strive to approximate the

entire system with a smaller number of dimensions compared to the original

system. Here, the effectiveness of MOR in approximating the behavior of all

the variables in the original system by simulating a model with a radically

reduced dimension, is demonstrated.

In the present work, mathematical MOR is applied in the context of an experimentally verified signaling pathway model of plasticity (Kim et al., PLoS Comp. Biol.,

2013). This nonlinear chemical equation based model describes the biochemical

calcium signaling steps required for plasticity and learning in the subcortical

area of the brain. In addition to nonlinear characteristics, the model includes

time-dependent terms which pose an additional challenge both computational

efficiency and reduction wise.

The MOR method employed in this study is Proper Orthogonal Decomposition with

Discrete Empirical Interpolation Method (POD+DEIM), a subspace projection

method for reducing the dimensionality of nonlinear systems (Chaturantabut et

al., SIAM, 2010). By applying these methods, the simulation time of the model

is radically shortened. However, our preliminary studies show approximation error if the model is simulated for a very long time. The tolerated amount of approximation error depends on the final application of the model. Based on these promising results, POD+DEIM is recommended for dimensionality reduction in computational neuroscience.

In summary, the reduced order model consumes a considerably smaller amount of computational resources than the original model, while maintaining a low root

mean square error between the variables in the original and reduced models.

This was achieved by simulating the system dynamics in a lower dimensional

subspace without losing any of the variables from the model. The results presented here are novel as mathematical MOR has not been studied in neuroscience without linearisation of the mathematical model and never in the context of the model presented here.

1. Kim, B., Hawes, S.L., Gillani, F., Wallace, L.J. and Blackwell, K.T., 2013.

Signaling pathways involved in striatal synaptic plasticity are sensitive to

temporal pattern and exhibit spatial specificity. PLoS computational

biology, 9(3), p.e1002953.

2. Chaturantabut, S. and Sorensen, D.C., 2010. Nonlinear model reduction via

discrete empirical interpolation. SIAM Journal on Scientific Computing,

32(5), pp.2737-2764

Alkuperäiskieli | Englanti |
---|---|

Artikkeli | P130 |

Sivut | 66-66 |

Julkaisu | BMC Neuroscience |

Vuosikerta | 19 |

Numero | Suppl 2 |

DOI - pysyväislinkit | |

Tila | Julkaistu - 29 syysk. 2018 |

Tapahtuma | Annual Computational Neuroscience meeting (CNS*2018): annual meeting of organization for computational neurosciences - University of Washington, Seattle, Yhdysvallat Kesto: 13 heinäk. 2018 → 18 heinäk. 2018 https://www.cnsorg.org/cns-2018-meeting-program |

## !!ASJC Scopus subject areas

- Neuroscience (miscellaneous)