Improvement in capture rate 〈<i>C</i>_<i>r</i>〉 due to motion noise <i>D</i>_<i>m</i> and the noise of the targets motion <i>D</i>_<i>p</i>.

<p>The ensemble average of the capture rate 〈<i>C</i>_<i>r</i>〉 as a function of <i>D</i>_<i>p</i> and <i>D</i>_<i>m</i> with internal neural noise <i>D</i>_<i>s</i> = 1 × 10⁻³ <b>(A)</b> and <i>D</i>_<i>s</i> = 5 × 10⁻³ <b>(B)</b>. The other parameters are <i>K</i> = 5, <i>g</i> = 10⁻², <i>b</i> = 0.24, <i>N</i> = 100, and <i>θ</i> = 1. The peak 〈<i>C</i>_<i>r</i>〉 is distributed roughly along the line <i>D</i>_<i>p</i> + <i>D</i>_<i>m</i> = 0.4 in (A) and <i>D</i>_<i>p</i> + <i>D</i>_<i>m</i> = 0.3 in (B). It is clear that the maximization of 〈<i>C</i>_<i>r</i>〉 requires a balance among <i>D</i>_<i>m</i>, <i>D</i>_<i>s</i>, and <i>D</i>_<i>p</i>. Numerical 〈<i>C</i>_<i>r</i>〉 are computed from 400 trials of a numerical simulation.</p

Behavioral SR of an agent driven by a simple non-neural PI controller.

<p><b>(A)</b> Improvement in the goal-reaching success rate due to additive motion noise. Numerical 〈<i>P</i>_<i>R</i>〉 are computed from 40 trials of a 500 s numerical simulation with <i>K</i>_<i>I</i> = 0.01 and <i>θ</i> = 0.1. Error bars in <b>(A)</b> indicate standard deviations. <b>(B1–B3)</b> Capture-rate improvement due to motion additive noise. Numerical 〈<i>C</i>_<i>r</i>〉 are computed from 1,000 trials. The parameters for <b>(B1–B3)</b> are <i>K</i>_<i>I</i> = 0.02 × 10⁻² and <i>θ</i> = 2.</p

Theoretical analysis of RSRI.

<p><b>(A)</b> Schematic model of a feedback-controlled Brownian particle agent. The agent has an end-effector of size <i>θ</i> used to reach a target moving along the pre-designed path <i>x</i>_<i>g</i>(<i>t</i>). For simplicity, we assume that <i>x</i>_<i>g</i>(<i>t</i>) is periodic. <b>(B,C,D)</b> Plot of theoretical 〈<i>P</i>_<i>R</i>〉 with contour lines versus the moving target frequency <i>f</i> and the agent motion noise intensity <i>D</i> computed using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188298#pone.0188298.e006" target="_blank">Eq (5)</a> with <i>θ</i> = 0.01 <b>(B)</b>, <i>θ</i> = 0.1 <b>(C)</b>, and <i>θ</i> = 1 <b>(D)</b>. <b>(E)</b> <i>B</i> with respect to <i>f</i> and <i>A</i> = 0.1, 1, 2, 3 with <i>ϵ</i> = 1. Note that with <i>t</i> = 1/<i>f</i>, lim_{<i>f</i>→∞} <i>B</i> = <i>A</i> cos(1). <b>(F)</b> 〈<i>P</i>_<i>R</i>〉 with respect to <i>D</i> × 10 and <i>θ</i> = 0.2, 0.4, 0.5, 0.55, 0.65, 0.8, 1 with <i>A</i> = 0.1.</p

Emergent aperiodic control signal and asynchronous neural firing.

<p><b>(S1, R1)</b> The input signal to the motion actuator <b>(S1)</b> and the corresponding neural firing rate <i>R</i>(<i>t</i>) − <i>R</i>₀ <b>(R1)</b>, with <i>D</i>_<i>s</i> = 0 and <i>D</i>_<i>m</i> = 0. Note that the input signal to the actuator is totally deterministic, although it exhibits jittering. In addition, the corresponding neural spikes are synchronized (the even vertical lines represent bursts of spikes, not individual spikes.) <b>(S2, R2)</b> An aperiodic and stochastic control signal emerges with either <i>D</i>_<i>m</i> > 0 or <i>D</i>_<i>s</i> > 0 <b>(S2)</b>. The corresponding firing rate becomes asynchronous if <i>D</i>_<i>s</i> > 0 <b>(R2)</b>.</p

Motion error with respect to <i>D</i>_<i>s</i>.

<p> with <i>g</i> = 0.02, <i>D</i>_<i>b</i> = 0.1, and <i>D</i>_<i>s</i> = 0.05 for <b>(A)</b>, and <i>g</i> = 0.5, <i>D</i>_<i>b</i> = 0.1, and <i>D</i>_<i>s</i> = 0.005 for <b>(B)</b>. The error bars indicate standard deviations. Note that we could not find any significant improvements due to the presence of force noise <i>D</i>_<i>m</i>. Numerical is computed from 500 trials of a 500 s numerical simulation, and the error bars correspond to the standard error.</p

Neurophysical agent design and task setup.

<p><b>(A, B)</b> Two different numerical simulation setups for studying behavioral NIO. In setup (A), we study the NIO when a neurophysical agent tracks along a static predesigned path. In setup (B), we study the NIO that occurs when the agent captures randomly moving (i.e., noisy) targets. In the second paradigm, we consider not only the additive neural and force noises internal to the subject agent, but also the motion noise of the moving target.</p

Capture rate modification by threshold size <i>θ</i>.

<p>The parameters are <i>D</i>_<i>s</i> = 1 × 10⁻³, <i>K</i> = 5, <i>b</i> = 0.24, <i>N</i> = 100, and <i>g</i> = 0.01. The rate of improvement in capture rate is dependent on the size of the geometric threshold <i>θ</i>. Numerical 〈<i>C</i>_<i>r</i>〉 are computed from 100 trials of a 500 s numerical simulation, and error bars indicate standard errors and are within the symbols.</p

Neural activity taken from the intermediate visuo-tactile map during observation of a facial expression: surprise (red frame) and stare (green frame).

<p>We present a sequence of facial expressions from surprise to stare and vice-versa. The selected bimodal neuron taken from the intermediate map triggers to the characteristic visual configurational patterns of the face during rapid changes, which permits to detect the mouth and eyes movements. this behavior is due to the sensory alignment and of the high correlation with the tactile distribution of its own face. Note: the subject has given written informed consent to publication of his photograph.</p