The prototyping and the development of computational codes for biological models, in terms of reliability, efficient and portable building blocks allow to simulate real cerebral behaviours and to validate theories and experiments. A critical issue is the tuning of a model by means of several numerical simulations with the aim to reproduce real scenarios. This requires a huge amount of computational resources to assess the impact of parameters that influence the neuronal response. In this paper, we describe how parallel tools are adopted to simulate the socalled depolarization block of a CA1 pyramidal cell of hippocampus. Here, the high performance computing techniques are adopted in order to achieve a more efficient model simulation. Finally, we analyse the performance of this neural model, investigating the scalability and benefits on multi-core and on parallel and distributed architectures. © The Authors. Published by Elsevier B.V.

Parallel tools for simulating the depolarization block on a neural model

Ponti, G.
2015

Abstract

The prototyping and the development of computational codes for biological models, in terms of reliability, efficient and portable building blocks allow to simulate real cerebral behaviours and to validate theories and experiments. A critical issue is the tuning of a model by means of several numerical simulations with the aim to reproduce real scenarios. This requires a huge amount of computational resources to assess the impact of parameters that influence the neuronal response. In this paper, we describe how parallel tools are adopted to simulate the socalled depolarization block of a CA1 pyramidal cell of hippocampus. Here, the high performance computing techniques are adopted in order to achieve a more efficient model simulation. Finally, we analyse the performance of this neural model, investigating the scalability and benefits on multi-core and on parallel and distributed architectures. © The Authors. Published by Elsevier B.V.
Parallel computing;Computational neuroscience;Multi-core computing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/5760
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