Limits...
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.


Percent overlap as a function of motor intent and spike frequency (Simulation run 4). These plots present data from a set of simulations using different motoneurons pools (S, FF, and mixed S & FF) that provide signals to a set of LIFEs. S motoneurons had spike durations of 4 ms and had firing frequencies that ranged from 5 to 18 Hz over the lower half of the motor intent range; FF motoneurons had spike durations of 2 ms and had firing frequencies that ranged from 18 to 35 Hz over the upper half of the motor intent range. 15 electrodes were simulated with different combinations of fiber type (all S, all FF, or a mix of S & FF) and number of neurons contributing (2, 4, 6, 8, 10). Percent overlap represents the percentage of the recording time in which there was overlap of 2 or more spikes. Composite frequency was calculated as the total number of spikes summed across all neurons that contribute to a particular electrode. Plot (A) shows the percent overlap on recordings from LIFE electrodes as a function of motor intent. Note that percent overlap is higher for electrodes that record from more neurons and that it increases as a function of motor intent. Plot (B) presents results from the same set of simulations, but with the data plotted as a function of composite frequency. On the plots, the black, red, and blue lines/markers indicate values derived from electrodes that record from S, mixed and FF motoneurons, respectively. Note that with this specification of motoneurons (spike duration and rates), the highest value for percent overlap is less than 20% and that electrodes that record signals from S motoneurons have higher values of spike overlap for a given composite frequency than those that record from a mixed population or from only FF motoneurons, because of the difference in spike durations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4231878&req=5

Figure 10: Percent overlap as a function of motor intent and spike frequency (Simulation run 4). These plots present data from a set of simulations using different motoneurons pools (S, FF, and mixed S & FF) that provide signals to a set of LIFEs. S motoneurons had spike durations of 4 ms and had firing frequencies that ranged from 5 to 18 Hz over the lower half of the motor intent range; FF motoneurons had spike durations of 2 ms and had firing frequencies that ranged from 18 to 35 Hz over the upper half of the motor intent range. 15 electrodes were simulated with different combinations of fiber type (all S, all FF, or a mix of S & FF) and number of neurons contributing (2, 4, 6, 8, 10). Percent overlap represents the percentage of the recording time in which there was overlap of 2 or more spikes. Composite frequency was calculated as the total number of spikes summed across all neurons that contribute to a particular electrode. Plot (A) shows the percent overlap on recordings from LIFE electrodes as a function of motor intent. Note that percent overlap is higher for electrodes that record from more neurons and that it increases as a function of motor intent. Plot (B) presents results from the same set of simulations, but with the data plotted as a function of composite frequency. On the plots, the black, red, and blue lines/markers indicate values derived from electrodes that record from S, mixed and FF motoneurons, respectively. Note that with this specification of motoneurons (spike duration and rates), the highest value for percent overlap is less than 20% and that electrodes that record signals from S motoneurons have higher values of spike overlap for a given composite frequency than those that record from a mixed population or from only FF motoneurons, because of the difference in spike durations.

Mentions: Figure 10 presents the calculated degree of spike overlap vs. frequency of firing of motoneurons across a set of simulations using several electrode and motor intent settings (Simulation run 4). Figure 10A presents spike overlap as a function of motor intent for each of the electrodes. The plots demonstrate that percent overlap increases as a result of increased motor intent and the number of axons that contribute to a particular electrode. Note that the overlap in electrodes that record solely from S fibers reaches a plateau at motor intent = 0.5 (since this value was specified as the saturation point for that motor pool); the electrodes that record solely from FF fibers show overlap only for values of motor intent greater than 0.5 (since this value was specified as the threshold value point for that motor pool); and the electrodes that record from a combination of S and FF show a gradual increase in spike overlap throughout the range. Also note that the maximum values recorded for spike overlap was approximately the same for the three groups of electrodes (S, FF, and S & FF) due to the fact that the effect on spike overlap of lower firing rates of the S axons was offset by their longer spike durations. This effect is also demonstrated in Figure 10B, which demonstrates that overlap on electrodes that recorded from S axons increased more rapidly as a function of composite firing rate than those that recorded from FF axons; the rate of increase in overlap for the S & FF electrodes was at an intermediate level.


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)

Percent overlap as a function of motor intent and spike frequency (Simulation run 4). These plots present data from a set of simulations using different motoneurons pools (S, FF, and mixed S & FF) that provide signals to a set of LIFEs. S motoneurons had spike durations of 4 ms and had firing frequencies that ranged from 5 to 18 Hz over the lower half of the motor intent range; FF motoneurons had spike durations of 2 ms and had firing frequencies that ranged from 18 to 35 Hz over the upper half of the motor intent range. 15 electrodes were simulated with different combinations of fiber type (all S, all FF, or a mix of S & FF) and number of neurons contributing (2, 4, 6, 8, 10). Percent overlap represents the percentage of the recording time in which there was overlap of 2 or more spikes. Composite frequency was calculated as the total number of spikes summed across all neurons that contribute to a particular electrode. Plot (A) shows the percent overlap on recordings from LIFE electrodes as a function of motor intent. Note that percent overlap is higher for electrodes that record from more neurons and that it increases as a function of motor intent. Plot (B) presents results from the same set of simulations, but with the data plotted as a function of composite frequency. On the plots, the black, red, and blue lines/markers indicate values derived from electrodes that record from S, mixed and FF motoneurons, respectively. Note that with this specification of motoneurons (spike duration and rates), the highest value for percent overlap is less than 20% and that electrodes that record signals from S motoneurons have higher values of spike overlap for a given composite frequency than those that record from a mixed population or from only FF motoneurons, because of the difference in spike durations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Percent overlap as a function of motor intent and spike frequency (Simulation run 4). These plots present data from a set of simulations using different motoneurons pools (S, FF, and mixed S & FF) that provide signals to a set of LIFEs. S motoneurons had spike durations of 4 ms and had firing frequencies that ranged from 5 to 18 Hz over the lower half of the motor intent range; FF motoneurons had spike durations of 2 ms and had firing frequencies that ranged from 18 to 35 Hz over the upper half of the motor intent range. 15 electrodes were simulated with different combinations of fiber type (all S, all FF, or a mix of S & FF) and number of neurons contributing (2, 4, 6, 8, 10). Percent overlap represents the percentage of the recording time in which there was overlap of 2 or more spikes. Composite frequency was calculated as the total number of spikes summed across all neurons that contribute to a particular electrode. Plot (A) shows the percent overlap on recordings from LIFE electrodes as a function of motor intent. Note that percent overlap is higher for electrodes that record from more neurons and that it increases as a function of motor intent. Plot (B) presents results from the same set of simulations, but with the data plotted as a function of composite frequency. On the plots, the black, red, and blue lines/markers indicate values derived from electrodes that record from S, mixed and FF motoneurons, respectively. Note that with this specification of motoneurons (spike duration and rates), the highest value for percent overlap is less than 20% and that electrodes that record signals from S motoneurons have higher values of spike overlap for a given composite frequency than those that record from a mixed population or from only FF motoneurons, because of the difference in spike durations.
Mentions: Figure 10 presents the calculated degree of spike overlap vs. frequency of firing of motoneurons across a set of simulations using several electrode and motor intent settings (Simulation run 4). Figure 10A presents spike overlap as a function of motor intent for each of the electrodes. The plots demonstrate that percent overlap increases as a result of increased motor intent and the number of axons that contribute to a particular electrode. Note that the overlap in electrodes that record solely from S fibers reaches a plateau at motor intent = 0.5 (since this value was specified as the saturation point for that motor pool); the electrodes that record solely from FF fibers show overlap only for values of motor intent greater than 0.5 (since this value was specified as the threshold value point for that motor pool); and the electrodes that record from a combination of S and FF show a gradual increase in spike overlap throughout the range. Also note that the maximum values recorded for spike overlap was approximately the same for the three groups of electrodes (S, FF, and S & FF) due to the fact that the effect on spike overlap of lower firing rates of the S axons was offset by their longer spike durations. This effect is also demonstrated in Figure 10B, which demonstrates that overlap on electrodes that recorded from S axons increased more rapidly as a function of composite firing rate than those that recorded from FF axons; the rate of increase in overlap for the S & FF electrodes was at an intermediate level.

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.