One of the important goals of peripheral nerve electrode development is to design an electrode for selective recruitment of the different functions of a common nerve trunk. A challenging task is gaining selective access to central axon populations. In this paper, a simple electrode that takes advantage of the neural plasticity to reshape the nerve is presented. The flat interface nerve electrode (FINE) reshapes the nerve into a flat geometry to increase the surface area and move central axon populations close to the surface. The electrode was implanted acutely on the sciatic nerve of eight cats. The FINE can significantly reshape the nerve and fascicles (p<0.0001) while maintaining the same total nerve cross-sectional area. The stimulation thresholds were 2.89 nC for pulse amplitude modulation and 10.2 nC for pulsewidth modulation. Monopolar, square-pulse stimulation with single contacts on the FINE selectively recruited each of the four main branches of the sciatic nerve. Simultaneous stimulation with two contacts produced moments about the ankle joint that were a combination of the moments produced by the individual contacts when stimulated separately.

We have developed a method to predict excitation of axons based on the response of passive models. An expression describing the transmembrane potential induced in passive models to an applied electric field is presented. Two terms were found to drive the polarization of each node. The first was a source term described by the activating function at the node, and the other was an ohmic term resulting from redistribution of current from sources at other nodes. A total equivalent driving function including both terms was then defined. We found that the total equivalent driving function can be used to provide accurate predictions of excitation thresholds for any applied field. The method requires only knowledge of the intracellular strength-duration relationship of the axon, the passive step response of the axon to an intracellular current, and the values of the extracellular potentials. Excitation thresholds for any given applied field can then be calculated using a simple algebraic expression. This method eliminates the errors associated with use of the activating function alone, and greatly reduces the computation required to determine fiber response to applied extracellular fields.

Stochastic resonance (SR) is a phenomenon observed in nonlinear systems whereby the introduction of noise enhances the detection of a subthreshold signal for a certain range of noise intensity. The nonlinear threshold detection mechanism that neurons employ and the noisy environment in which they reside makes it likely that SR plays a role in neural signal detection. Although the role of SR in sensory neural systems has been studied extensively, its role in central neurons is unknown. In many central neurons, such as the hippocampal CA1 cell, very large dendritic trees are responsible for detecting neural input in a noisy environment. Attenuation due to the electrotonic length of these trees is significant, suggesting that a method other than passive summation is necessary if signals at the distal ends of the tree are to be detected. The hypothesis that SR plays an important role in the detection of distal synaptic inputs first was tested in a computer simulation of a CA1 cell and then verified with in vitro rat hippocampal slices. The results clearly showed that SR can enhance signal detection in CA1 hippocampal cells. Moreover, high levels of noise were found to equalize detection of synaptic signals received at varying positions on the dendritic tree. The amount of noise needed to evoke the effect is compared with physiological noise in slices and in vivo.