2019
DOI: 10.3389/fninf.2019.00032
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A Brief History of Simulation Neuroscience

Abstract: Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this revie… Show more

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Cited by 62 publications
(44 citation statements)
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References 275 publications
(334 reference statements)
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“…Our "replicate and explain" approach differs from the typical computational route taken to study circuit dynamics, whereby simplified circuit models are constructed to study the dependence of an observed phenomenon, e.g., SSA, on unobserved biophysical parameters 38,39 , for example, synaptic depression. While this classical modeling approach is invaluable for testing specific mechanisms that might explain the observed phenomena, the model is a priori tailored to replicate the phenomenon and, by design, it ignores other putative underlying mechanisms (and the interactions among them) that might impact the studied phenomena 40 .…”
Section: Discussionmentioning
confidence: 99%
“…Our "replicate and explain" approach differs from the typical computational route taken to study circuit dynamics, whereby simplified circuit models are constructed to study the dependence of an observed phenomenon, e.g., SSA, on unobserved biophysical parameters 38,39 , for example, synaptic depression. While this classical modeling approach is invaluable for testing specific mechanisms that might explain the observed phenomena, the model is a priori tailored to replicate the phenomenon and, by design, it ignores other putative underlying mechanisms (and the interactions among them) that might impact the studied phenomena 40 .…”
Section: Discussionmentioning
confidence: 99%
“…The same model partitioned across 64 cores required only 17 min. For a detailed discussion of several other large-scale simulation efforts, see also (De Garis et al, 2010; Fan and Markram, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…Concepts developed through work with algorithms, neural networks, and reinforcement learning may provide new approaches that help better understand brain-based intelligence, while artificial neural networks may serve as simulations that provide insights which help to better understand brain processes. Simulation neuroscience, a new research field that aims to build a digital copy of the brain, relies heavily on these interdisciplinary connections and fosters collaborations between researchers from neuroscience and computer science (Markram 2006;Fan and Markram 2019).…”
Section: Brain-based and Artificial Intelligencementioning
confidence: 99%