Learning Control of a Flight Simulator Stick

Aimportant part of a flight simulator is its\ud control loading system, which is the part\ud that emulates the behaviour of an aircraft as\ud experienced by the pilot through the stick.\ud Such a system consists of a model of the\ud aircraft that is to be simulated and a stick that is\ud driven by an electric motor. To make the\ud simulation as realistic as possible, the\ud simulator stick should behave in the same way\ud as the stick in the real aircraft.\ud However, due to the properties of the motor\ud and the stick, small irregularities can be felt\ud when the stick is moved, which do not occur in\ud a real aircraft. Probable causes of these\ud irregularities are cogging in the motor and\ud small imperfections in the transmission.\ud Both disturbances have a reproducible nature.\ud Because the disturbances are reproducible,\ud feedback error learning control is used for\ud control. The learning controller consists of two\ud neural networks. One neural network is used to\ud compensate the unknown friction and is\ud operated in feed-forward. The other neural\ud network compensates cogging and\ud imperfections in the transmission and is\ud operated in feedback. Experimental results\ud showed that the learning controller is able to\ud compensate the disturbances

Spike-Interval Triggered Averaging Reveals a Quasi-Periodic Spiking Alternative for Stochastic Resonance in Catfish Electroreceptors

<div><p>Catfish detect and identify invisible prey by sensing their ultra-weak electric fields with electroreceptors. Any neuron that deals with small-amplitude input has to overcome sensitivity limitations arising from inherent threshold non-linearities in spike-generation mechanisms. Many sensory cells solve this issue with stochastic resonance, in which a moderate amount of intrinsic noise causes irregular spontaneous spiking activity with a probability that is modulated by the input signal. Here we show that catfish electroreceptors have adopted a fundamentally different strategy. Using a reverse correlation technique in which we take spike interval durations into account, we show that the electroreceptors generate a supra-threshold bias current that results in quasi-periodically produced spikes. In this regime stimuli modulate the interval between successive spikes rather than the instantaneous probability for a spike. This alternative for stochastic resonance combines threshold-free sensitivity for weak stimuli with similar sensitivity for excitations and inhibitions based on single interspike intervals.</p> </div

The Filter-LIF model.

<p>(A) Schematic diagram. The model consists of a linear band-pass filter (<i>F(t)</i>), taking the stimulus (<i>S(t)</i>) plus added noise (<i>N₁(t)</i>) as its input, followed by a standard LIF spike generation mechanism. The LIF spike generator performs a leaky integration of the filter output plus a second noise source (<i>N₂(t)</i>). This second noise source corresponds to a high frequency noise on the spike threshold. If the integrated signal crosses the threshold level (<i>h</i>), a spike is generated and the integrator is reset to a value of −100. (B) Model behavior for quasi-periodic spiking. (C) The behavior in a regime where spike generation is strongly driven by noise and external input signals. The linear filter stages were the same for both simulations, except for a gain factor. The top panels show examples of output signals from the linear filters. The second row displays the course of the ‘membrane potential’, including reset and post-spike recovery. The two regimes differ in the setting for the threshold (blue line) relative to the resting level (green line) and reset level (red line). For deterministic firing (B) the threshold is set well below the resting level, whereas for input-driven firing (C) it is set above the resting level. The bottom row panels represent the SITAs, with interval classes corresponding to the colors in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032786#pone-0032786-g001" target="_blank">Fig. 1</a>.</p

Two-interval SITAs.

<p>Each panel shows a single class of intervals subdivided according to the preceding interval. Interval durations are indicated by the insets in each graph. The black horizontal lines in the inset show the mean duration of the interval immediately preceding the trigger spike (at t = 0). The colored lines represent mean interval durations for the preceding intervals, with colors corresponding to the different curves. Color codes are similar to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032786#pone-0032786-g001" target="_blank">Figure 1</a>. Thick lines correspond to measured data, thin lines to predictions based on linear summation of separate and independent SITAs for the two consecutive intervals. In calculating the linear sum of the SITAs for the first of the two intervals we used a time shift equal to the mean interval duration for the second interval (black horizontal line in insets). Linear predictions and actual measurements are highly similar, indicating that adding a second interval to the analysis provides no information that was not already present in the single interval analysis.</p

Bode plots comparing reverse correlation data and sinusoidal stimulation data for an example electroreceptor.

<p>(A) Amplitudes, (B) Phases. Grey lines with symbols show experimental measurements for sinusoidal stimulation. The other lines are based on Fourier transforms of STA and SITA data (see legend). The inset in (A) shows the example SITAs from the reverse correlation data that were used for these Fourier transforms.</p

Reverse correlation results at different stimulus amplitudes.

<p>Experimental data (left column) and model simulations (right column). Model predictions were based on simulations with model parameters that were obtained by fitting the model to data from a standard reverse correlation experiment (third row of data, amplitude of 1). Noise amplitudes were varied by a factor of two between successive rows. Model simulations and actual measurements show very similar effects. At small stimulus amplitudes, SITAs for long and short intervals have similar shapes and comparable latencies. At higher stimulus amplitudes, shapes and latencies for different interval classes change drastically. Typically, inhibitory deflections become delayed relative to excitatory deflections and they may generate a short latency excitatory peak.</p

Spike interval triggered averaging (SITA).

<p>(A) The recording setup. Stimulation currents are applied locally through a stimulation ring, while spikes from afferent are recorded from within the lumen of the electroreceptor. (B) Example of the reverse correlation technique. For each recorded spike the stimulus shape is analyzed in a directly preceding time interval (-Δt). For each measurement about 75,000 spikes were grouped in 5 classes with equal numbers of spikes in each class, based on the cumulative distribution of interspike interval durations (intervals preceding spikes). (C) Interspike interval distribution (bottom panel) and cumulative interspike interval distribution (top panel). Spike-triggered averages were generated for each class separately. (D) Overview of spike interval triggered averages (SITAs) for 26 electroreceptors recorded in 20 catfish. Colors indicate the different interval classes as shown in (C). Confidence intervals for each class represent ±2*SEM. The overall STA is shown in black and the moment of the trigger spike is represented by the dashed line at T = 0 ms. (E) Left hand panel: Distributions of peak amplitude values, normalized to the amplitude for the shortest interval class. Right hand panel: distribution of peak latencies.</p