身体性システムの知覚運動系における生物情動規範型ノイズ・ゆらぎ活用メカニズムに関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 國吉 康夫, 東京大学教授 山本 義春, 東京大学教授 神崎 亮平, 東京大学教授 廣瀬 通孝, 東京大学教授 原田 達也University of Tokyo(東京大学

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Bodily motion fluctuation improves reaching success rate in a neurophysical agent via geometric-stochastic resonance.

Organisms generate a variety of noise types, including neural noise, sensory noise, and noise resulting from fluctuations associated with movement. Sensory and neural noises are known to induce stochastic resonance (SR), which improves information transfer to the subjects control systems, including the brain. As a consequence, sensory and neural noise provide behavioral benefits, such as stabilization of posture and enhancement of feeding efficiency. In contrast, the benefits of fluctuations in the movements of a biological system remain largely unclear. Here, we describe a novel type of noise-induced order (NIO) that is realized by actively exploiting the motion fluctuations of an embodied system. In particular, we describe the theoretical analysis of a feedback-controlled embodied agent system that has a geometric end-effector. Furthermore, through several numerical simulations we demonstrate that the ratio of successful reaches to goal positions and capture of moving targets are improved by the exploitation of motion fluctuations. We report that reaching success rate improvement (RSRI) is based on the interaction of the geometric size of an end-effector, the agents motion fluctuations, and the desired motion frequency. Therefore, RSRI is a geometrically induced SR-like phenomenon. We also report an interesting result obtained through numerical simulations indicating that the agents neural and motion noise must be optimized to match the prey's motion noise in order to maximize the capture rate. Our study provides a new understanding of body motion fluctuations, as they were found to be the active noise sources for a behavioral NIO

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

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

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

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 change due to the presence of neural and force noises.

<p><b>(A, B)</b> 〈<i>d</i>_<i>e</i>〉 and with the parameters <i>T</i> = 10, <i>K</i> = 10, <i>g</i> = 0.02. <b>(C)</b> 〈<i>d</i>_<i>e</i>〉 with <i>D</i>_<i>m</i> = 0.01 and 〈<i>e</i>_<i>m</i>〉, with <i>D</i>_<i>m</i> = 0.05 as a function of <i>D</i>_<i>s</i>. <b>(D)</b> 〈<i>ρ</i>〉 and 〈<i>N</i>_<i>o</i>〉 as a function of <i>D</i>_<i>s</i>. <b>(E, F)</b> The change in motion trajectory due to the presence of neural and motion noises, with <i>T</i> = 10, <i>K</i> = 10, and <i>g</i> ≪ 1 [<b>(E)</b>] and <i>g</i> ≫ 0 [<b>(F)</b>]. The inset is an enlargement of the respective areas inside the rectangles. Note that the bias variability <i>D</i>_<i>b</i> ≫ 0 leads to high pooling ability and reduces the oscillatory motion, but obscures the neuronal SR effect. Furthermore, the motion accuracy achieved due to neuronal SR (with <i>D</i>_<i>b</i> ∼ 0 and <i>D</i>_<i>s</i> > 0) is higher than it is in the noiseless system with high motor pooling ability (with <i>D</i>_<i>b</i> ≫ 0 and <i>D</i>_<i>s</i> = 0) <b>(G)</b>. Numerical 〈<i>d</i>_<i>e</i>〉, , 〈<i>ρ</i>〉, and 〈<i>N</i>_<i>o</i>〉 are computed from 500 trials of a 500 s numerical simulation.</p