A bio-friendly and economical technique for chronic implantation of multiple microelectrode arrays

Chhatbar, Pratik Y.; Kraus, Lee M. von; Semework, Mulugeta; Francis, Joseph T.

doi:10.1016/j.jneumeth.2010.02.006

Cited by 32 publications

(25 citation statements)

References 12 publications

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“…The ability to move individual electrodes can dramatically improve the yield of single units, as demonstrated by the comparison of the unit activity between electrodes that had or had not been moved across pairs of successive sessions. The single-unit yields in the initial recording sessions of each implant were similar to those reported for experiments using immovable electrode arrays, chronically implanted in multiple cortical areas (Chhatbar et al 2010;Nicolelis et al 2003). However, the ability to move electrodes at will over the lifetime of the implant led to sustained yields over the long term, in marked contrast to the time-dependent deterioration of yields typically seen with immovable electrode arrays Muthuswamy et al 2011).…”

Section: Discussionsupporting

confidence: 69%

A system for recording neural activity chronically and simultaneously from multiple cortical and subcortical regions in nonhuman primates

Feingold

Desrochers

Fujii

et al. 2012

Journal of Neurophysiology

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A major goal of neuroscience is to understand the functions of networks of neurons in cognition and behavior. Recent work has focused on implanting arrays of ∼100 immovable electrodes or smaller numbers of individually adjustable electrodes, designed to target a few cortical areas. We have developed a recording system that allows the independent movement of hundreds of electrodes chronically implanted in several cortical and subcortical structures. We have tested this system in macaque monkeys, recording simultaneously from up to 127 electrodes in 14 brain regions for up to one year at a time. A key advantage of the system is that it can be used to sample different combinations of sites over prolonged periods, generating multiple snapshots of network activity from a single implant. Used in conjunction with microstimulation and injection methods, this versatile system represents a powerful tool for studying neural network activity in the primate brain.

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Section: Discussionsupporting

confidence: 69%

A system for recording neural activity chronically and simultaneously from multiple cortical and subcortical regions in nonhuman primates

Feingold

Desrochers

Fujii

et al. 2012

Journal of Neurophysiology

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“…S1 cortex was implanted with an array of electrodes (96 channel Utah array, Blackrock Microsystems) [8]. Peristimulus time histograms (PSTH) of neural spiking data were generated for two time intervals by aligning spike times either with the timing of a visual cue relaying the impending result of a given trial (reward will be delivered or reward will be withheld) or with the timing of the trial’s outcome (reward delivered or reward withheld) and extended 500 milliseconds after the stimulus of interest using a 100 millisecond bin width.…”

Section: Methodsmentioning

confidence: 99%

Classifier Performance in Primary Somatosensory Cortex Towards Implementation of a Reinforcement Learning Based Brain Machine Interface

McNiel

Bataineh

Choi

et al. 2016

2016 32nd Southern Biomedical Engineering Conference (SBEC)

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Increasingly accurate control of prosthetic limbs has been made possible by a series of advancements in brain machine interface (BMI) control theory. One promising control technique for future BMI applications is reinforcement learning (RL). RL based BMIs require a reinforcing signal to inform the controller whether or not a given movement was intended by the user. This signal has been shown to exist in cortical structures simultaneously used for BMI control. This work evaluates the ability of several common classifiers to detect impending reward delivery within primary somatosensory (S1) cortex during a grip force match to sample task performed by a nonhuman primate. The accuracy of these classifiers was further evaluated over a range of conditions to identify parameters that provide maximum classification accuracy. S1 cortex was found to provide highly accurate classification of the reinforcement signal across many classifiers and a wide variety of data input parameters. The classification accuracy in S1 cortex between rewarding and non-rewarding trials was apparent when the animal was expecting an impending delivery or an impending withholding of reward following trial completion. The high accuracy of classification in S1 cortex can be used to adapt an RL based BMI towards a user’s intent. Real-time implementation of these classifiers in an RL based BMI could be used to adapt control of a prosthesis dynamically to match the intent of its user.

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“…Following successful training the animal was implanted in primary somatosensory (S1) cortex with a microelectrode array (96-channel Utah array, 1.0mm length, Blackrock Microsystems) [5,6]. Following a healing period of two weeks, neural activity was recorded using a multi-acquisition processor system (MAP, Plexon Inc., Dallas, TX) controlled by a Windows PC.…”

Section: Introductionmentioning

confidence: 99%

Reward value is encoded in primary somatosensory cortex and can be decoded from neural activity during performance of a psychophysical task

McNiel

Choi

Hessburg

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Encoding of reward valence has been shown in various brain regions, including deep structures such as the substantia nigra as well as cortical structures such as the orbitofrontal cortex. While the correlation between these signals and reward valence have been shown in aggregated data comprised of many trials, little work has been done investigating the feasibility of decoding reward valence on a single trial basis. Towards this goal, one non-human primate (macaca radiata) was trained to grip and hold a target level of force in order to earn zero, one, two, or three juice rewards. The animal was informed of the impending result before reward delivery by means of a visual cue. Neural data was recorded from primary somatosensory cortex (S1) during these experiments and firing rate histograms were created following the appearance of the visual cue and used as input to a variety of classifiers. Reward valence was decoded with high levels of accuracy from S1 both in the post-cue and post-reward periods. Additionally, the proportion of units showing significant changes in their firing rates was influenced in a predictable way based on reward valence. The existence of a signal within S1 cortex that encodes reward valence could have utility for implementing reinforcement learning algorithms for brain machine interfaces. The ability to decode this reward signal in real time with limited data is paramount to the usability of such a signal in practical applications.

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A bio-friendly and economical technique for chronic implantation of multiple microelectrode arrays

Cited by 32 publications

References 12 publications

A system for recording neural activity chronically and simultaneously from multiple cortical and subcortical regions in nonhuman primates

A system for recording neural activity chronically and simultaneously from multiple cortical and subcortical regions in nonhuman primates

Classifier Performance in Primary Somatosensory Cortex Towards Implementation of a Reinforcement Learning Based Brain Machine Interface

Reward value is encoded in primary somatosensory cortex and can be decoded from neural activity during performance of a psychophysical task

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