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.

Field programmable gate arrays (FPGAs) have previously been shown as high-performance platforms for neural-modeling applications. Implementations have traditionally been time-consuming and error-prone, requiring the neural modeler to work outside of their expert domain. This paper demonstrates a new approach to the development of neural models using an auto-generation toolkit. This design tool enables model construction-level alterations (e.g., adjustment of model population size or insertion/deletion of an ionic conductance) within hours and parameter changes on-the-fly. The approach is validated on a 40-neuron pre-Bötzinger complex population model consisting of Hodgkin-Huxley style conductances and fully interconnected synapses. A total of 1880 parameters are on-the-fly user tunable on a free-running model. The resulting implemented model performs at a theoretical 8.7 x real-time utilizing 90% of logic elements within a Xilinx Virtex-4 XC4VSX35-fg676-10 FPGA.

Physiologically realistic models of motoneurons are often simulated using traditional software modeling tools, with suboptimal performance. This paper describes the successful implementation of a multi-compartment motoneuron model demonstrating plateau potentials on a Field Programmable Gate Array (FPGA). Simulation performance exceeds 4x realtime, an over 12x improvement over a software simulation. This demonstrates the capability of FPGAs in executing complex dynamical systems with high throughput.