A control algorithm for autonomous optimization of extracellular recordings

This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed

A Miniature Robot for Isolating and Tracking Neurons in Extracellular Cortical Recordings

This paper presents a miniature robot device and control algorithm that can autonomously position electrodes in cortical tissue for isolation and tracking of extracellular signals of individual neurons. Autonomous electrode positioning can significantly enhance the efficiency and quality of acute electrophysiolgical experiments aimed at basic understanding of the nervous system. Future miniaturized systems of this sort could also overcome some of the inherent difficulties in estabilishing long-lasting neural interfaces that are needed for practical realization of neural prostheses. The paper describes the robot's design and summarizes the overall structure of the control system that governs the electrode positioning process. We present a new sequential clustering algorithm that is key to improving our system's performance, and which may have other applications in robotics. Experimental results in macaque cortex demonstrate the validity of our approach

Spike Clustering and Neuron Tracking over Successive Time Windows

This paper introduces a new methodology for tracking signals from individual neurons over time in multiunit extracellular recordings. The core of our strategy relies upon an extension of a traditional mixture model approach, with parameter optimization via expectation-maximimization (EM), to incorporate clustering results from the preceding time period in a Bayesian manner. EM initialization is also achieved by utilizing these prior clustering results. After clustering, we match the current and prior clusters to track persisting neurons. Applications of this spike sorting method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results

An Algorithm for Autonomous Isolation of Neurons in Extracellular Recordings

This paper describes novel multi-electrode systems that can autonomously position recording electrodes inside cortical tissue so as to isolate and then maintain optimal extracellular signal recording quality without human intervention. Autonomous microdrives can be used to improve the quality and efficiency of acute recordings that are needed for basic research in neurophysiology. They also offer the potential to increase the longevity and quality of chronic recordings and will serve as the front end of neuroprosthetic systems that aid the handicapped. We first describe the autonomous positioning algorithm, and its implementation as a finite state machine. We have deployed the algorithm on both conventional acute recording micro-drives and a novel miniature robot microdrive. Experimental results in monkey cortex are presented

Bayesian clustering and tracking of neuronal signals for autonomous neural interfaces

This paper introduces a new, unsupervised method for sorting and tracking the non-stationary spike signals of individual neurons in multi-unit extracellular recordings. While this method may be applied to a variety of problems that arise in the field of neural interfaces, its development is motivated by a new class of autonomous neural recording devices. The core of the proposed strategy relies upon an extension of a traditional expectation-maximization (EM) mixture model optimization to incorporate clustering results from the preceding recording interval in a Bayesian manner. Explicit filtering equations for the case of a Gaussian mixture are derived. Techniques using prior data to seed the EM iterations and to select the appropriate model class are also developed. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. Applications of this signal classification method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results than traditional methods

Recording advances for neural prosthetics

An important challenge for neural prosthetics research is to record from populations of neurons over long periods of time, ideally for the lifetime of the patient. Two new advances toward this goal are described, the use of local field potentials (LFPs) and autonomously positioned recording electrodes. LFPs are the composite extracellular potential field from several hundreds of neurons around the electrode tip. LFP recordings can be maintained for longer periods of time than single cell recordings. We find that similar information can be decoded from LFP and spike recordings, with better performance for state decodes with LFPs and, depending on the area, equivalent or slightly less than equivalent performance for signaling the direction of planned movements. Movable electrodes in microdrives can be adjusted in the tissue to optimize recordings, but their movements must be automated to be a practical benefit to patients. We have developed automation algorithms and a meso-scale autonomous electrode testbed, and demonstrated that this system can autonomously isolate and maintain the recorded signal quality of single cells in the cortex of awake, behaving monkeys. These two advances show promise for developing very long term recording for neural prosthetic applications

A Method for Neuronal Source Identification

Multi-sensor microelectrodes for extracellular action potential recording have significantly improved the quality of in vivo recorded neuronal signals. These microelectrodes have also been instrumental in the localization of neuronal signal sources. However, existing neuron localization methods have been mostly utilized in vivo, where the true neuron location remains unknown. Therefore, these methods could not be experimentally validated. This article presents experimental validation of a method capable of estimating both the location and intensity of an electrical signal source. A four-sensor microelectrode (tetrode) immersed in a saline solution was used to record stimulus patterns at multiple intensity levels generated by a stimulating electrode. The location of the tetrode was varied with respect to the stimulator. The location and intensity of the stimulator were estimated using the Multiple Signal Classification (MUSIC) algorithm, and the results were quantified by comparison to the true values. The localization results, with an accuracy and precision of ~ 10 microns, and ~ 11 microns respectively, imply that MUSIC can resolve individual neuronal sources. Similarly, source intensity estimations indicate that this approach can track changes in signal amplitude over time. Together, these results suggest that MUSIC can be used to characterize neuronal signal sources in vivo.Comment: 14 pages, 5 figure

A miniature robot for autonomous single neuron recordings

This paper describes a novel miniature robot that can autonomously position recording electrodes inside cortical tissue to isolate and maintain optimal extracellular action potential recordings. The system consists of a novel motorized miniature recording microdrive and a control algorithm. The microdrive was designed for semi-chronic operation and can independently position four electrodes with micron precision over a 5mm range using small (3mm diameter) piezoelectric linear actuators. The autonomous positioning algorithm is designed to detect, align and cluster action potentials, and then command the microdrive to optimize and maintain the neural signal. This system is shown to be capable of autonomous operation in monkey cortex

Probing the dynamics of identified neurons with a data-driven modeling approach

In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach