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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.


Examples of spike templates. Three spike morphologies with normalized amplitudes between (− 1, 1) and normalized duration between (0, 1) are scaled in time and amplitude to form a multitude of spike templates. A spike template is a characteristic of a neuron. Spike morphologies are classified in terms symmetry and the number of peaks and troughs. Plots (A–C) present spike morphologies that are: symmetric with one peak and one trough, symmetric with two peaks and two troughs, and asymmetric with one peak and one trough. Other spike morphologies are possible and can be directly programmed in the simulator. After scaling in amplitude and time, each spike morphology can be used to generate several spike templates, as shown in plots (D–F), each of which has three spike templates generated from one spike morphology.
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Figure 5: Examples of spike templates. Three spike morphologies with normalized amplitudes between (− 1, 1) and normalized duration between (0, 1) are scaled in time and amplitude to form a multitude of spike templates. A spike template is a characteristic of a neuron. Spike morphologies are classified in terms symmetry and the number of peaks and troughs. Plots (A–C) present spike morphologies that are: symmetric with one peak and one trough, symmetric with two peaks and two troughs, and asymmetric with one peak and one trough. Other spike morphologies are possible and can be directly programmed in the simulator. After scaling in amplitude and time, each spike morphology can be used to generate several spike templates, as shown in plots (D–F), each of which has three spike templates generated from one spike morphology.

Mentions: In the simulator, spike shapes are specified by the user in a process that includes several steps. First, the user selects normalized spike morphologies (Figure 5). Spike morphologies are generated by differentiating Gaussian and Gamma functions, which can produce a variety of spike wavelets similar to spike shapes reported in the literature. The spike wavelets are normalized in amplitudes between (−1, 1) and normalized in duration between (0, 1). The spike-morphologies are then scaled in amplitude and duration by the simulator using parameters that can be specified by the user (Figure 2C).


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)

Examples of spike templates. Three spike morphologies with normalized amplitudes between (− 1, 1) and normalized duration between (0, 1) are scaled in time and amplitude to form a multitude of spike templates. A spike template is a characteristic of a neuron. Spike morphologies are classified in terms symmetry and the number of peaks and troughs. Plots (A–C) present spike morphologies that are: symmetric with one peak and one trough, symmetric with two peaks and two troughs, and asymmetric with one peak and one trough. Other spike morphologies are possible and can be directly programmed in the simulator. After scaling in amplitude and time, each spike morphology can be used to generate several spike templates, as shown in plots (D–F), each of which has three spike templates generated from one spike morphology.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Examples of spike templates. Three spike morphologies with normalized amplitudes between (− 1, 1) and normalized duration between (0, 1) are scaled in time and amplitude to form a multitude of spike templates. A spike template is a characteristic of a neuron. Spike morphologies are classified in terms symmetry and the number of peaks and troughs. Plots (A–C) present spike morphologies that are: symmetric with one peak and one trough, symmetric with two peaks and two troughs, and asymmetric with one peak and one trough. Other spike morphologies are possible and can be directly programmed in the simulator. After scaling in amplitude and time, each spike morphology can be used to generate several spike templates, as shown in plots (D–F), each of which has three spike templates generated from one spike morphology.
Mentions: In the simulator, spike shapes are specified by the user in a process that includes several steps. First, the user selects normalized spike morphologies (Figure 5). Spike morphologies are generated by differentiating Gaussian and Gamma functions, which can produce a variety of spike wavelets similar to spike shapes reported in the literature. The spike wavelets are normalized in amplitudes between (−1, 1) and normalized in duration between (0, 1). The spike-morphologies are then scaled in amplitude and duration by the simulator using parameters that can be specified by the user (Figure 2C).

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.