Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits
have become essential tools in modern brain research. The goal of these multiscale models …

Addressing the multiscale organization of the brain, which is fundamental to the dynamic
repertoire of the organ, remains challenging. In principle, it should be possible to model …

In recent years, large-scale computational models of the cortex have emerged as a powerful
way to study the multi-scale mechanisms of neural processing. However, due to …

Experimental studies in neuroscience are producing data at a rapidly increasing rate,
providing exciting opportunities and formidable challenges to existing theoretical and …

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing
and disparate experimental datasets at multiple scales. The NetPyNE tool (www. netpyne …

The possibility to replicate and reproduce published research results is one of the biggest
challenges in all areas of science. In computational neuroscience, there are thousands of …

For constructing neuronal network models computational neuroscientists have access to
wide-ranging anatomical data that nevertheless tend to cover only a fraction of the …

Computational models are powerful tools for exploring the properties of complex biological
systems. In neuroscience, data-driven models of neural circuits that span multiple scales are …

Simulations of neural activity at different levels of detail are ubiquitous in modern
neurosciences, aiding the interpretation of experimental data and underlying neural …

Highlights•Computational models will prove increasingly useful for understanding large
datasets.•Substantial challenges exist for fitting detailed models to data.•Conceptual and …