Discrete stimulus estimation from neural responses in the turtle retina

Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods

In a natural environment, sensory systems are faced with ever-changing stimuli that can occur, disappear or change their properties at any time. For the animal to react adequately the sensory systems must be able to detect changes in external stimuli based on its neuronal responses. Since the nervous system has no prior knowledge of the stimulus timing, changes in stimulus need to be inferred from the changes in neuronal activity, in particular increase or decrease of the spike rate, its variability, and shifted response latencies. From a mathematical point of view, this problem can be rephrased as detecting changes of statistical properties in a time series. In neuroscience, the CUSUM (cumulative sum) method has been applied to recorded neuronal responses for detecting a single stimulus change. Here, we investigate the applicability of the CUSUM approach for detecting single as well as multiple stimulus changes that induce increases or decreases in neuronal activity. Like the nervous system, our algorithm relies exclusively on previous neuronal population activities, without using knowledge about the timing or number of external stimulus changes. We apply our change point detection methods to experimental data obtained by multi-electrode recordings from turtle retinal ganglion cells, which react to changes in light stimulation with a range of typical neuronal activity patterns. We systematically examine how variations of mathematical assumptions (Poisson, Gaussian, and Gamma distributions) used for the algorithms may affect the detection of an unknown number of stimulus changes in our data and compare these CUSUM methods with the standard Rate Change method. Our results suggest which versions of the CUSUM algorithm could be useful for different types of specific data sets.

A new high-density (25 electrodes/mm²) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures

OBJECTIVE

Among the currently available neural interface devices, there has been a need for a penetrating electrode array with a high electrode-count and high electrode-density (the number of electrodes/mm(2)) that can be used for electrophysiological studies of sub-millimeter neuroanatomical structures. We have developed such a penetrating microelectrode array with both a high electrode-density (25 electrodes/mm(2)) and high electrode-count (up to 96 electrodes) for small nervous system structures, based on the existing Utah Slanted Electrode Array (USEA). Such high electrode-density arrays are expected to provide greater access to nerve fibers than the conventionally spaced USEA especially in small diameter nerves.

APPROACH

One concern for such high density microelectrode arrays is that they may cause a nerve crush-type injury upon implantation. We evaluated this possibility during acute (<10 h) in vivo experiments with electrode arrays implanted into small diameter peripheral nerves of anesthetized rats (sciatic nerve) and cats (pudendal nerve).

MAIN RESULTS

Successful intrafascicular implantation and viable nerve function was demonstrated via microstimulation, single-unit recordings and histological analysis. Measurements of the electrode impedances and quantified electrode dimensions demonstrated fabrication quality. The results of these experiments show that such high density neural interfaces can be implanted acutely into neural tissue without causing a complete nerve crush injury, while mediating intrafascicular access to fibers in small diameter peripheral nerves.

SIGNIFICANCE

This new penetrating microelectrode array has characteristics un-matched by other neural interface devices currently available for peripheral nervous system neurophysiological research.