NEAT is a python library for the study, simulation and simplification of morphological neuron models. NEAT accepts morphologies in the de facto standard .swc format [Cannon1998], and implements high-level tools to interact with and analyze the morphologies. NEAT also allows for the convenient definition of morphological neuron models. These models can be simulated, through an interface with the NEURON simulator [Carnevale2004], or can be analyzed with two classical methods: (i) the separation of variables method [Major1993] to obtain impedance kernels as a superposition of exponentials and (ii) Koch's method to compute impedances with linearized ion channels analytically in the frequency domain [Koch1985]. Furthermore, NEAT implements the neural evaluation tree framework [Wybo2019] and an associated C++ simulator, to analyze subunit independence. Finally, NEAT implements a new and powerful method to simplify morphological neuron models into compartmental models with few compartments [Wybo2020]. For these models, NEAT also provides a NEURON interface so that they can be simulated directly, and will soon also provide a NEST interface [Gewaltig2007].


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