MIIND is a simulator that allows the creation, simulation and analysis of large-scale neural networks. It does not model individual neurons, but models populations directly, similarly to a neural mass model, except that we use population density techniques. Population density techniques are based on point model neurons, such as leaky-integrate-and-fire (LIF), quadratic-integrate-and-fire neurons (QIF), or more complex ones, such as adaptive-exponential-integrate-and-fire (AdExp), Izhikevich, Fitzhugh-Nagumo (FN). MIIND is able to model populations of 1D neural models (like LIF, QIF), 2D models (AdExp, Izhikevich, FN, others), or 3D models (Hindmarsh-Rose, Tsodyks-Markram Synapse). It does so by using statistical techniques to answer the question: "If I'd run a NEST or BRIAN simulation (to name some point model-based simulators), where in state space would my neurons be?" We calculate this distribution in terms of a density function, and from this density function we can infer many properties of the population, including its own firing rate. By modeling large-scale networks as homogeneous populations that exchange firing rate statistics, rather than spikes, remarkable efficiency can be achieved, whilst retaining a connection to spiking neurons that is not present in neural mass models.
MIIND is available through pypi and can be installed on most Linux and Windows systems (Mac version in development) with the command:
$ python -m pip install miind
For building from source and further documentation:
https://miind.readthedocs.io/en/latest/
They said it could not be done, but we have created an efficient version of the Hindmarsh rose model,
a neural model with three state variables.
