This is the weblink for the models and algorithms associated with the paper Mechler F, Victor JD (2012) Dipole characterization of single neurons from their extracellular action potentials. J Comput Neurosci Authors: Ferenc Mechler & Jonathan Victor e-mail: fmechler@med.cornell.edu The DipoleLocalizationKit archives in its 3 parts: (i) Matlab code of the neuron localization algorithm; (ii) the lead fields (and lead potentials) of 4 Thomas tetrodes precomputed in a FEM model and saved in Matlab [struct] format; and (iii) real extracellular action potential (EAP) data from 3 single neurons in visual cortex, plus the results of the localization algorithm applied to them; all in Matlab [struct] format. An enclosed ReadMe document explains the use of these tools and data. We localize a single neuron from the spatial sample of its EAP amplitudes recorded with a multisite probe (with 6 or more independent measurement sites or channels, e.g., a silicon polytrode, a stepped tetrode, etc.) This is an inverse problem and we solve it by fitting a model to the EAPs that consists of a volume conductor model of the neural tissue (known), a realistic model of the probe (known), and a single dipole current source of the model neuron (unknown). The dipole is free to change position, size, and orientation (a total of 6 parameters) at each moment during the action potential. This algorithm numerically solves the dipole optimization problem on a discrete grid in two, separable, stages. The first stage, a linear dipole moment optimization (location is fixed), is solved everywhere on the grid using the lead fields (LFs). The LFs are required input to this algorithm and assumed to be pre-calculated. (Software to compute LFs is not provided here. LFs can be calculated by, e.g., the finite element method; for details of LF computations, see Mechler & Victor (2012).) The second stage, a nonlinear global optimization of the source location, is solved using Tikhonov regularization.