Blog
About

1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence. Despite rapid advances in the formalized description of models, data, and analysis workflows, there is no accepted consensus regarding the terminology and practical implementation of validation workflows in the context of neural simulations. This situation prevents the generic, unbiased comparison between published models, which is a key element of enhancing reproducibility of computational research in neuroscience. In this study, we argue for the establishment of standardized statistical test metrics that enable the quantitative validation of network models on the level of the population dynamics. Despite the importance of validating the elementary components of a simulation, such as single cell dynamics, building networks from validated building blocks does not entail the validity of the simulation on the network scale. Therefore, we introduce a corresponding set of validation tests and present an example workflow that practically demonstrates the iterative model validation of a spiking neural network model against its reproduction on the SpiNNaker neuromorphic hardware system. We formally implement the workflow using a generic Python library that we introduce for validation tests on neural network activity data. Together with the companion study (Trensch et al., 2018), the work presents a consistent definition, formalization, and implementation of the verification and validation process for neural network simulations.

          Related collections

          Most cited references 65

          • Record: found
          • Abstract: found
          • Article: not found

          Simple model of spiking neurons.

           E Izhikevich (2002)
          A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            The earth is round (p < .05).

             Jacob Cohen V (1994)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The asynchronous state in cortical circuits.

              Correlated spiking is often observed in cortical circuits, but its functional role is controversial. It is believed that correlations are a consequence of shared inputs between nearby neurons and could severely constrain information decoding. Here we show theoretically that recurrent neural networks can generate an asynchronous state characterized by arbitrarily low mean spiking correlations despite substantial amounts of shared input. In this state, spontaneous fluctuations in the activity of excitatory and inhibitory populations accurately track each other, generating negative correlations in synaptic currents which cancel the effect of shared input. Near-zero mean correlations were seen experimentally in recordings from rodent neocortex in vivo. Our results suggest a reexamination of the sources underlying observed correlations and their functional consequences for information processing.
                Bookmark

                Author and article information

                Affiliations
                1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre , Jülich, Germany
                2Theoretical Systems Neurobiology, RWTH Aachen University , Aachen, Germany
                3Simulation Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Jülich Research Centre , Jülich, Germany
                Author notes

                Edited by: Robert Andrew McDougal, Yale University, United States

                Reviewed by: Richard C. Gerkin, Arizona State University, United States; Tadashi Yamazaki, University of Electro-Communications, Japan; Salvador Dura-Bernal, SUNY Downstate Medical Center, United States; Boris Marin, Universidade Federal do ABC, Brazil

                *Correspondence: Robin Gutzen r.gutzen@ 123456fz-juelich.de
                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                19 December 2018
                2018
                : 12
                10.3389/fninf.2018.00090
                6305903
                Copyright © 2018 Gutzen, von Papen, Trensch, Quaglio, Grün and Denker.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Counts
                Figures: 9, Tables: 1, Equations: 11, References: 71, Pages: 19, Words: 14767
                Funding
                Funded by: Horizon 2020 10.13039/501100007601
                Award ID: 720270
                Award ID: 785907
                Funded by: Helmholtz Association 10.13039/501100009318
                Award ID: ZT-I-0003
                Categories
                Neuroscience
                Original Research

                Comments

                Comment on this article