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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

Model for simulating the activity on peripheral nerve electrodes during motor tasks. The model consists of three components shown in (A) the motoneuron activation unit, the motoneuron output unit, and the electrode unit. The input to the model is a vector of motor intent signals, u(t), which is first transformed to activation states of motoneurons, x(t), then to motoneuron outputs, y(t). The motoneuron output signals combine with noise, W(t), to produce the vector of signals, z(t), recorded by the electrodes. The motoneuron output model includes three components shown in (B): the firing rate of a motoneuron is determined by its activation state and its firing rate mapping function, the time series of pulses is the output of a point process which is then convolved with the spike template to produce the motoneuron output signals, y(t). (C) Illustrates the production of spike templates with various temporal and geometric characteristics. A spike shape is selected at random from a pool of spikes of different morphologies Ψ. Then the selected spike is scaled in time by the function Λ and in amplitude by Ai.
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Figure 2: Model for simulating the activity on peripheral nerve electrodes during motor tasks. The model consists of three components shown in (A) the motoneuron activation unit, the motoneuron output unit, and the electrode unit. The input to the model is a vector of motor intent signals, u(t), which is first transformed to activation states of motoneurons, x(t), then to motoneuron outputs, y(t). The motoneuron output signals combine with noise, W(t), to produce the vector of signals, z(t), recorded by the electrodes. The motoneuron output model includes three components shown in (B): the firing rate of a motoneuron is determined by its activation state and its firing rate mapping function, the time series of pulses is the output of a point process which is then convolved with the spike template to produce the motoneuron output signals, y(t). (C) Illustrates the production of spike templates with various temporal and geometric characteristics. A spike shape is selected at random from a pool of spikes of different morphologies Ψ. Then the selected spike is scaled in time by the function Λ and in amplitude by Ai.

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)

Model for simulating the activity on peripheral nerve electrodes during motor tasks. The model consists of three components shown in (A) the motoneuron activation unit, the motoneuron output unit, and the electrode unit. The input to the model is a vector of motor intent signals, u(t), which is first transformed to activation states of motoneurons, x(t), then to motoneuron outputs, y(t). The motoneuron output signals combine with noise, W(t), to produce the vector of signals, z(t), recorded by the electrodes. The motoneuron output model includes three components shown in (B): the firing rate of a motoneuron is determined by its activation state and its firing rate mapping function, the time series of pulses is the output of a point process which is then convolved with the spike template to produce the motoneuron output signals, y(t). (C) Illustrates the production of spike templates with various temporal and geometric characteristics. A spike shape is selected at random from a pool of spikes of different morphologies Ψ. Then the selected spike is scaled in time by the function Λ and in amplitude by Ai.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Model for simulating the activity on peripheral nerve electrodes during motor tasks. The model consists of three components shown in (A) the motoneuron activation unit, the motoneuron output unit, and the electrode unit. The input to the model is a vector of motor intent signals, u(t), which is first transformed to activation states of motoneurons, x(t), then to motoneuron outputs, y(t). The motoneuron output signals combine with noise, W(t), to produce the vector of signals, z(t), recorded by the electrodes. The motoneuron output model includes three components shown in (B): the firing rate of a motoneuron is determined by its activation state and its firing rate mapping function, the time series of pulses is the output of a point process which is then convolved with the spike template to produce the motoneuron output signals, y(t). (C) Illustrates the production of spike templates with various temporal and geometric characteristics. A spike shape is selected at random from a pool of spikes of different morphologies Ψ. Then the selected spike is scaled in time by the function Λ and in amplitude by Ai.
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