Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

Neuroscience currently lacks a comprehensive theory of how cognitive processes can be
implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes …

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network
simulations using biologically realistic connectivity. This simulation environment enables the …

Computational neuroscience has produced a diversity of software for simulations of
networks of spiking neurons, with both negative and positive consequences. On the one …

Biologically detailed single neuron and network models are important for understanding
how ion channels, synapses and anatomical connectivity underlie the complex electrical …

Large-scale spiking neural network (SNN) simulations are challenging to implement, due to
the memory and computation required to iteratively process the large set of neural state …

Introduction Studies of categorical decision-making attempt to understand behavior by
probing how different features of complex and changing environments guide the selection of …

The development of neural tissue is a complex organizing process, in which it is difficult to
grasp how the various localized interactions between dividing cells leads relentlessly to …

We present PyCARL, a PyNN-based common Python programming interface for hardware-
software co-simulation of spiking neural network (SNN). Through PyCARL, we make the …

An important open problem of computational neuroscience is the generic organization of
computations in networks of neurons in the brain. We show here through rigorous theoretical …