The estimation of action potential thresholds is a subjective process, which we quantified by surveying experienced electrophysiologists via a software application that allowed them to select action potential thresholds from several presented neuronal time series. Independent of this survey, we derived two nonparametric techniques for automating the detection of an action potential threshold from the time-series of intracellular recordings. Both methods start with a phase-space representation of the action potential (dV/dt versus V). Method I detects the maximum slope in the phase space, while Method II detects the maximum second derivative in the phase space. These two methods, as well as five additional methods in the literature, were tested on three data sets representing a variety of action potential shapes, the same three datasets that were used in the electrophysiologist survey. The database of user responses was used to provide an external benchmark against which to statistically evaluate all seven methods. Method II, as well as the curvature-based Methods VI and VII, provided the best results tracking both absolute and relative changes in threshold versus the other nonparametric methods (peak of second and third time derivatives). The one parametric method evaluated, detection of threshold crossing of the first temporal derivative, performed comparably to these methods, provided that an appropriate threshold was chosen. We conclude that Methods II, VI, and VII were the best methods evaluated due to their performance across a wide range of action potential shapes and the fact that they are nonparametric. Our user database of responses may be useful to other investigators interested in developing additional methods in that it quantifies what has often been a subjective estimate.

A minimal neuron model, the Morris-Lecar model, is implemented on field programmable analog arrays (FPAAs). Our approach is to solve the differential equation describing the model in a similar way a computer solves the same problem: numerically integrate the differential equation by making arithmetic operations on voltage mode circuits of the FPAAs. The results demonstrate that biologically relevant dynamics can be observed from the electronic neuron despite limitations on the configurability of the FPAAs. Such models can be run accurately in real-time or many orders of magnitude faster than real-time. FPAAs are feasible candidates for implementation of neuron models using off-the-shelf software-reconfigurable analog circuit elements.

A half-center neural oscillator was coupled to a simple mechanical system to study the closed-loop interactions between a central pattern generator and its effector muscles. After a review of the open-loop mechanisms that were previously introduced by Skinner et al. (1994), we extend their geometric approach and introduce four additional closed-loop mechanisms by the inclusion of an antagonistic muscle pair acting on a mass and connected to the half-center neural oscillator ipsilaterally. Two of the closed-loop mechanisms, mechanical release mechanisms, have close resemblance to open-loop release mechanisms whereas the latter two, afferent mechanisms, have a strong dependence on the mechanical properties of the system. The results also show that stable oscillations can emerge in the presence of sensory feedback even if the neural system is not oscillatory. Finally, the feasibility of the closed-loop mechanisms was shown by weakening the idealized assumptions of the synaptic and the feedback connections as well as the rapidity of the oscillations.