Scientific models are abstractions that aim to explain natural phenomena. A successful
model shows how a complex phenomenon arises from relatively simple principles while …

Brian 2 allows scientists to simply and efficiently simulate spiking neural network models.
These models can feature novel dynamical equations, their interactions with the …

A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …

Research on dendrites has been conducted for decades, providing valuable information for
the development of dendritic computation. Creating an ideal neuron model is crucial for …

There is a high diversity of neuronal types in the mammalian neocortex. To facilitate
construction of system models with multiple cell types, we generate a database of point …

Neurons encode information in sequences of spikes, which are triggered when their
membrane potential crosses a threshold. In vivo, the spiking threshold displays large …

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

A large variety of neuron models are used in theoretical and computational neuroscience,
and among these, single-compartment models are a popular kind. These models do not …

Modern graphics cards contain hundreds of cores that can be programmed for intensive
calculations. They are beginning to be used for spiking neural network simulations. The goal …

Computational modeling is increasingly used to understand the function of neural circuits in
systems neuroscience. These studies require models of individual neurons with realistic …