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A functional model and simulation of spinal motor pools and intrafascicular recordings of motoneuron activity in peripheral nerve.

Abdelghani MN, Abbas JJ, Horch KW, Jung R - Front Neurosci (2014)

Bottom Line: As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs.In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes.The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.

View Article: PubMed Central - PubMed

Affiliation: Adaptive Neural Systems Lab, Department of Biomedical Engineering, Florida International University Miami, FL, USA.

ABSTRACT
Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.

No MeSH data available.


Related in: MedlinePlus

Schematic organization of motor control system and recording of motor activity with a LIFE. Motor intent (I: I1, I2, I3) can be represented as a multi-dimensional signal from centers in the brain to motoneurons pools in the spinal cord, which produce firing patterns in motoneurons (M: M1, M2, M3). Axons from a given motor pool tend to cluster together along the length of the peripheral nerve fascicle. The diagram shows a LIFE electrode that has been implanted into one of the fascicles of the nerve.
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Figure 1: Schematic organization of motor control system and recording of motor activity with a LIFE. Motor intent (I: I1, I2, I3) can be represented as a multi-dimensional signal from centers in the brain to motoneurons pools in the spinal cord, which produce firing patterns in motoneurons (M: M1, M2, M3). Axons from a given motor pool tend to cluster together along the length of the peripheral nerve fascicle. The diagram shows a LIFE electrode that has been implanted into one of the fascicles of the nerve.

Mentions: A model and simulation system were developed to simulate the activity of motoneuron pools based on a multi-DOF input of motor intent. Figure 1 presents a schematic that represents the system that is modeled in which multiple LIFEs are implanted in peripheral nerve of an amputee to record activity of motoneurons that is driven by motor intent signals. The simulator (Figures 2, 3) consists of three primary components: the motoneuron activation unit, the motoneuron output unit and the electrode unit. Each of these is described in the sections that follow.


A functional model and simulation of spinal motor pools and intrafascicular recordings of motoneuron activity in peripheral nerve.

Abdelghani MN, Abbas JJ, Horch KW, Jung R - Front Neurosci (2014)

Schematic organization of motor control system and recording of motor activity with a LIFE. Motor intent (I: I1, I2, I3) can be represented as a multi-dimensional signal from centers in the brain to motoneurons pools in the spinal cord, which produce firing patterns in motoneurons (M: M1, M2, M3). Axons from a given motor pool tend to cluster together along the length of the peripheral nerve fascicle. The diagram shows a LIFE electrode that has been implanted into one of the fascicles of the nerve.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231878&req=5

Figure 1: Schematic organization of motor control system and recording of motor activity with a LIFE. Motor intent (I: I1, I2, I3) can be represented as a multi-dimensional signal from centers in the brain to motoneurons pools in the spinal cord, which produce firing patterns in motoneurons (M: M1, M2, M3). Axons from a given motor pool tend to cluster together along the length of the peripheral nerve fascicle. The diagram shows a LIFE electrode that has been implanted into one of the fascicles of the nerve.
Mentions: A model and simulation system were developed to simulate the activity of motoneuron pools based on a multi-DOF input of motor intent. Figure 1 presents a schematic that represents the system that is modeled in which multiple LIFEs are implanted in peripheral nerve of an amputee to record activity of motoneurons that is driven by motor intent signals. The simulator (Figures 2, 3) consists of three primary components: the motoneuron activation unit, the motoneuron output unit and the electrode unit. Each of these is described in the sections that follow.

Bottom Line: As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs.In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes.The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.

View Article: PubMed Central - PubMed

Affiliation: Adaptive Neural Systems Lab, Department of Biomedical Engineering, Florida International University Miami, FL, USA.

ABSTRACT
Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.

No MeSH data available.


Related in: MedlinePlus