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Biologically Inspired Robotics
14.3 Materials and Methods
The experiments were carried out with single-unit metal microelectrodes
and patch clamp recordings in the acute animal preparation of the decerebrated cat as described in Bengtsson and Jörntell (2009) and Jörntell and
Ekerot (2003). Primary afferent axons were recorded on their pathway into
the cuneate nucleus and cuneate neurons were recorded inside the cuneate nucleus. Stimuli were delivered in two different ways. The first, which
was used to produce the model through the system identification, was a
standardized manual skin stimulation. A miniature strain-gauge device
was mounted on the tip of the investigator in order to control that the same
amount of force and the same stimulation time were used. A second mode
of stimulus was electrical skin stimulation applied through a pair of needle
electrodes inserted into the skin with a spacing of 3 mm. Stimulation intensity was 1.0 mA with a duration 0.1 ms.
As for empirical model estimation, standard methods and validation methods of system identification were used (Johansson and Magnusson 1991).
Then, the recorded data were processed using an action potential pattern recognition software, in order to reduce noise. For every action potential, only the time after stimulation was determined, transforming data to
spike-time data. This in turn could be added over several stimulations, yielding histogram data. This was then exported to MATLAB (The Mathworks,
Inc., Natick, MA, USA). For the mathematical modeling of the neuron transmission, the MATLAB System Identification Toolbox was used (Ljung 2002).
Various different model structures were tested—e.g., prediction error estimate (PEM), ARX, ARMAX, OE, BJ, N4SID (Ljung 2002)—to determine the
model that provided the most accurate representation. The starting point
was to find the model that most accurately represented the transformation
for a standardized manual skin stimulation. Subsequently, we simulated the
primary afferent input evoked by electrical skin stimulation and compared
the simulated cuneate neuron response with actual responses recorded from
the cuneate neuron with the same electrical skin stimulation.
14.4 Results
Figure 14.1 illustrates the spike responses of a primary afferent and a cuneate
neuron to manual skin stimulation. The data are displayed as peristimulus
histograms; that is, the same stimulation was repeated many times and the
spike counts for each bin represent the sum of spike responses for thirty to
fifty consecutive, nearly identical stimuli. All stimuli were aligned so that
they started at 0 ms and ended at 50 ms. The primary afferent, which conveys
Biologically Inspired Robotics
14.3 Materials and Methods
The experiments were carried out with single-unit metal microelectrodes
and patch clamp recordings in the acute animal preparation of the decerebrated cat as described in Bengtsson and Jörntell (2009) and Jörntell and
Ekerot (2003). Primary afferent axons were recorded on their pathway into
the cuneate nucleus and cuneate neurons were recorded inside the cuneate nucleus. Stimuli were delivered in two different ways. The first, which
was used to produce the model through the system identification, was a
standardized manual skin stimulation. A miniature strain-gauge device
was mounted on the tip of the investigator in order to control that the same
amount of force and the same stimulation time were used. A second mode
of stimulus was electrical skin stimulation applied through a pair of needle
electrodes inserted into the skin with a spacing of 3 mm. Stimulation intensity was 1.0 mA with a duration 0.1 ms.
As for empirical model estimation, standard methods and validation methods of system identification were used (Johansson and Magnusson 1991).
Then, the recorded data were processed using an action potential pattern recognition software, in order to reduce noise. For every action potential, only the time after stimulation was determined, transforming data to
spike-time data. This in turn could be added over several stimulations, yielding histogram data. This was then exported to MATLAB (The Mathworks,
Inc., Natick, MA, USA). For the mathematical modeling of the neuron transmission, the MATLAB System Identification Toolbox was used (Ljung 2002).
Various different model structures were tested—e.g., prediction error estimate (PEM), ARX, ARMAX, OE, BJ, N4SID (Ljung 2002)—to determine the
model that provided the most accurate representation. The starting point
was to find the model that most accurately represented the transformation
for a standardized manual skin stimulation. Subsequently, we simulated the
primary afferent input evoked by electrical skin stimulation and compared
the simulated cuneate neuron response with actual responses recorded from
the cuneate neuron with the same electrical skin stimulation.
14.4 Results
Figure 14.1 illustrates the spike responses of a primary afferent and a cuneate
neuron to manual skin stimulation. The data are displayed as peristimulus
histograms; that is, the same stimulation was repeated many times and the
spike counts for each bin represent the sum of spike responses for thirty to
fifty consecutive, nearly identical stimuli. All stimuli were aligned so that
they started at 0 ms and ended at 50 ms. The primary afferent, which conveys