TY  - JOUR
AU  - Trensch, Guido
AU  - Gutzen, Robin
AU  - Blundell, Inga
AU  - Denker, Michael
AU  - Morrison, Abigail
TI  - Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data
JO  - Frontiers in neuroinformatics
VL  - 12
SN  - 1662-5196
CY  - Lausanne
PB  - Frontiers Research Foundation
M1  - FZJ-2018-07146
SP  - 81
PY  - 2018
AB  - The reproduction and replication of scientific results is an indispensable aspect of good scientific practice, enabling previous studies to be built upon and increasing our level of confidence in them. However, reproducibility and replicability are not sufficient: an incorrect result will be accurately reproduced if the same incorrect methods are used. For the field of simulations of complex neural networks, the causes of incorrect results vary from insufficient model implementations and data analysis methods, deficiencies in workmanship (e.g., simulation planning, setup, and execution) to errors induced by hardware constraints (e.g., limitations in numerical precision). In order to build credibility, methods such as verification and validation have been developed, but they are not yet well-established in the field of neural network modeling and simulation, partly due to ambiguity concerning the terminology. In this manuscript, we propose a terminology for model verification and validation in the field of neural network modeling and simulation. We outline a rigorous workflow derived from model verification and validation methodologies for increasing model credibility when it is not possible to validate against experimental data. We compare a published minimal spiking network model capable of exhibiting the development of polychronous groups, to its reproduction on the SpiNNaker neuromorphic system, where we consider the dynamics of several selected network states. As a result, by following a formalized process, we show that numerical accuracy is critically important, and even small deviations in the dynamics of individual neurons are expressed in the dynamics at network level.
LB  - PUB:(DE-HGF)16
C6  - pmid:30534066
UR  - <Go to ISI:>//WOS:000451351200001
DO  - DOI:10.3389/fninf.2018.00081
UR  - https://juser.fz-juelich.de/record/858247
ER  -