Measuring Neuronal Signals with Microelectrode Arrays: A Finite Element Analysis

ObjectiveMeasuring neuronal cell activity using microelectrode arrays reveals a great variety of derived signal shapes within extracellular recordings. However, possible mechanisms responsible for this variety have not yet been entirely determined, which might hamper any subsequent analysis of the recorded neuronal data. For an investigation of this issue, we propose a computational model based on the finite element method describing the electrical coupling between an electrically active neuron and an extracellular recording electrode in detail. This allows for a systematic study of possible parameters that may play an essential role in defining or altering the shape of the measured electrode potential. Our results indicate that neuronal geometry and neurite structure, as well as the actual pathways of input potentials that evoke action potential generation, have a significant impact on the shape of the resulting extracellular electrode recording and explain most of the known signal shape variety.

Frequency-selective transmission of graded signals in large monopolar neurons of blowfly Calliphora vicina compound eye

The functional roles of voltage-gated K+ (Kv) channels in visual system interneurons remain poorly studied. We have addressed this problem in the large monopolar cells (LMCs) of the blowfly Calliphora vicina, using intracellular recordings and mathematical modeling methods. Intracellular recordings were performed in two cellular compartments: the synaptic zone, which receives input from photoreceptors, and the axon, which provides graded potential output to the third-order visual neurons. Biophysical properties of Kv conductances in the physiological voltage range were examined in the dark with injections of current in the discontinuous current-clamp mode. Putative LMC types 1/2 and 3 (L1/2 and L3, respectively) had dissimilar Kv channelomes: L1/2 displayed a prominent inactivating Kv conductance in the axon, while L3 cells were characterized by a sustained delayed-rectifier Kv conductance. To study the propagation of voltage signals, the data were incorporated into the previously developed mathematical model. We demonstrate that the complex interaction between the passive membrane properties, Kv conductances, and the neuronal geometry leads to a resonance-like filtering of signals with peak frequencies of transmission near 15 and 40 Hz for L3 and L1/2, respectively. These results point to distinct physiological roles of different types of LMCs.

Cytoskeletal control of neuronal geometry: A serial EM analysis

The cytoskeleton plays a direct role in controlling neurite shape. To quantitatively study both the three dimensional shape and the sub-micron structure of the cytoskeleton requires complete serial reconstruction at the Electron Microscopic level. We have devised a computer reconstruction system specifically for this purpose.The system uses a 35mm film copy of 3.25 x 4.00 inch EM negative as the data source. The film is placed into a high speed film transport (15 frames/second), which is mounted on a X,Y and rotation stage controlled by stepping motors. The 35mm film is viewed through a stepping motor controlled zoom lens mounted on a high resolution (1119 x 1024) video camera. A high resolution frame grabber controlled by the computer can store one complete frame. Thus, the live image and a stored image may be displayed alternately on a high resolution monitor. Finally, a graphics overlay and mouse connected to the computer can be used to align successive sections via the stepping motors, as well as to trace the outlines of a profile, or of a microtubule.

Postsynaptic neuronal geometry and synaptic effectiveness

Most central nervous system neurons receive synaptic input from hundreds or thousands of other neurons, and the computational function of such neurons results from the interactions of inputs on a large and complex scale. In most situations that have yielded to a partial analysis, the synaptic inputs to a neuron are not alike in function, but rather belong to distinct categories that differ qualitatively in the nature of their effect on the postsynaptic cell, and quantitatively in the strength of their influence. Many factors have been demonstrated to contribute to synaptic function, but one of the simplest and best known of these is the geometry of the postsynaptic neuron. The fundamental nature of the relationship between neuronal shape and synaptic effectiveness was established on theoretical grounds prior to its experimental verification.