MIIND: A Population-Level Neural Simulator Incorporating Stochastic Point Neuron Models

Definition

MIIND (Multiple Interacting Instantiations of Neural Dynamics) is a neural simulator that allows the creation of large-scale neuronal networks at the population level. Populations of neurons are considered to be homogeneous and comprised of point model neurons. MIIND does not simulate individual neurons but considers their distribution over the model neuron’s state space in terms of a density function and models the evolution of this density function in response to input from other neural populations or external input. From the density function, other quantities can be calculated, such as the population’s firing rate. This rate, in turn, can influence other populations. Because populations interact through firing rates rather than individual spikes, the simulation of networks of spiking neurons becomes easier as no events need to be buffered. Using an XML format, it is easy to configure large-scale network simulations. MIIND is implemented as a C++ package but has a Python...