simulator for spiking neural networks


Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include:
 - a system for specifying quantities with physical dimensions
 - exact numerical integration for linear differential equations
 - Euler, Runge-Kutta and exponential Euler integration for nonlinear
   differential equations
 - synaptic connections with delays
 - short-term and long-term plasticity (spike-timing dependent plasticity)
 - a library of standard model components, including integrate-and-fire
   equations, synapses and ionic currents
 - a toolbox for automatically fitting spiking neuron models to
   electrophysiological recordings

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Install python3-brian