A Robotic Neural Interface for Autonomous Positioning of Extracellular Recording Electrodes

Abstract

In this paper we describe a set of algorithms and a novel miniature device that together can autonomously position electrodes in neural tissue to obtain high-quality extracellular recordings. This robotic system moves each electrode to detect the signals of individual neurons, optimize the signal quality of a target neuron, and then maintain this signal over time. Such neuronal signals provide the key inputs for emerging neuroprosthetic medical devices and serve as the foundation of basic neuroscientific and medical research. Experimental results from extensive use of the robotic electrodes in macaque parietal cortex are presented to validate the method and to quantify its effectiveness.

Autonomous control for mechanically stable navigation of microscale implants in brain tissue to record neural activity

“…Further improvement of the algorithm to track individual signal units by using rigorous statistics and regression based algorithms as detailed by Wolf et al (2009), could be incorporated. Long periods of silence are often observed in the activity of a single neuron being recorded.…”

“…Past approaches in robotic controls for neural activity used stochastic models of neuronal activity patterns to position the microelectrodes to include tissue drifts among other factors that caused non-stationarities in neural recording to isolate and track specific units (Wolf et al 2009). The autonomous algorithm “SpikeTrack” builds a strong case that autonomous control could in fact isolate single units and maintain signal quality during recording sessions (Chakrabarti et al 2012).…”

Kalman filter mixture model for spike sorting of non-stationary data

“…We make the approximation that only the cluster mean varies significantly in time, in order to exploit the more computationally efficient Kalman filter methods emphasized here. Finally, Wolf et al (2009) recently employed an online Gaussian-based iterative tracking scheme which is similar in spirit to the forward recursion of the Kalman filter; these authors went further, implementing online hardware control of the electrode position to stabilize the recording quality. It would be interesting to explore whether the online expectation-maximization-Kalman approach we have presented here for tracking multiple clusters could lead to a more accurate and robust method for electrode stabilization.…”

A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability

“…Electrode drift is caused by the neuron movement relative to the recording electrode, as well as the change in the electrolytic property of the biological environment (Quiroga (2009)). Non-stationary waveforms refer to the change of the spike shape over time (Wolf et al (2009)). In CNS, Such variability problems have been investigated previously by modeling the source neurons as a mixture of Gaussians.…”