Neural networks

Many simulators exist that are aimed at simulating the interactions within (possibly large scale) networks of neurons. In these simulators, the neurons are usually represented at a fairly abstract level, such as integrate-and-fire or rate-based neurons. Amongst these are:

A simulator for spiking neural networks of integrate-and-fire or small compartment Hodgkin-Huxley neurons. It is written in Python and runs on many platforms.
Components And Tools for Accessible COMputer Modelling in Biology. CATACOMB 2 is a workbench for developing biologically plausible network models to perform behavioural tasks in virtual environments.
Formerly PDP++, this is a comprehensive simulation environment for creating complex, sophisticated models of the brain and cognitive processes using neural network models. Networks use computing units as used in artificial neural networks, which can represent rate-based neurons. Emergent includes a full GUI environment for constructing networks and the input/output patterns for the networks to process, and many different analysis tools for understanding what the networks are doing.
Fast Artificial Neural Network Library for simulating multilayer networks of artificial computing units.
Simulator for large scale neural systems. iqr provides an efficient graphical environment to design large-scale multi-level neuronal systems that can control real-world devices - robots in the broader sense - in real-time.
The light, efficient network simulator for running artificial neural network models.
NEural Simulation Technology for large-scale biologically realistic (spiking) neuronal networks. Neural models are usually point neurons, such as integrate-and-fire.
The Neural Simulation Language supports neural models having as a basic data structure neural layers with similar properties and similar connection patterns, where neurons are modelled as leaky integrators with connections subject to diverse learning rules.
Parallel neural Circuit SIMulator. A tool for simulating networks of millions of neurons and billions of synapses. Networks can be heterogeneous collections of different model spiking point neurons.