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Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models

Publications associées (57)

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The outstanding information processing capacity of the brain relies on numerous molecular mechanisms. It is an extremely energy-expensive function, which involves specialization and collaboration of different cell types. A common framework to study the com ...

EPFL2023

Data-driven whole mouse brain modeling for multi-scale simulations

Dimitri Bruno Marie Rodarie

In this thesis, we present a data-driven iterative pipeline to generate, simulate and validate point-neuron models of the whole mouse brain. The ultimate goal is to replicate close loop experiments with a virtual body in a virtual world. This pipeline was ...

EPFL2022

Computational Concepts for Reconstructing and Simulating Brain Tissue

Felix Schürmann, Srikanth Ramaswamy, Jean-Denis Georges Emile Courcol

It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These re ...

Springer2022

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Daniel Keller, Andrii Stepaniuk

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a ...

HUMANA PRESS INC2021

Simulation Tool : Resources Management in High Performance Avionic for ADR Missions

Jean-Paul Richard Kneib, Michaël Yannick Juillard

This paper describes the second iteration in the development of a simulation tool that provides trade-offs of avionic architectures for future Active Debris Removal (ADR) space missions. Challenges of ADR missions lay in their ability to first detect and t ...

IEEE2021

Fitting summary statistics of neural data with a differentiable spiking network simulator

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Shuqi Wang

Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is w ...

2021

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer perform ...

2020

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Robert Giterman, Andreas Toftegaard Kristensen

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

IEEE2020

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This paper deals with the analysis of reactor modulation experiments for code validation in the framework of the Horizon 2020 European project CORTEX. The analysis is based on a statistical based approach on spectral powers and their phase shift angles cal ...

2020

Understanding the link between the brain's anatomy and its function through computer simulations of neural tissue models is a widely used approach in computational neuroscience. This technique enables rapid prototyping and testing of hypotheses, allowing r ...

EPFL2019