Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer
Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer
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贪婪与社交：资源竞争振荡器网络作为基于阿米巴的神经计算机的模型
DOI：
10.1007/s11047-010-9224-y
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发表时间：
2011
影响因子：
2.1
通讯作者：
K. Aihara
中科院分区：
文献类型：
--
作者：
M. Aono;Yoshito Hirata;M. Hara;K. Aihara
多头绒泡菌是一种单细胞变形生物，具有丰富的时空振荡行为和复杂的计算能力。作者之前创造了一台生物计算机，将生物体作为计算底物来搜索组合优化问题的解决方案。借助于光学反馈实现递归神经网络模型，有机体通过交替生长和收回其感光分支来改变其形状，从而使其身体面积最大化，被光照的风险降至最低。这样，有机体以很高的概率成功地找到了四城旅行商问题的最优解。然而，目前还不清楚生物体如何使用振荡动力学来收集、存储和比较光刺激的信息。为了研究这些点，我们建立了一个基于变形虫的神经计算机的常微分方程式模型，将有机体视为一个竞争固定数量的细胞内资源的振荡器网络。这个模型被称为“资源竞争振荡器网络(RCON)模型”，它很好地再现了有机体通过实验观察到的行为，因为它通过保持资源的总和不变来产生一些时空振荡模式。通过合理设计反馈规则，RCON模型面临着资源向其节点的优化分配问题。在解决问题的过程中，具有最高竞争力的“贪婪”节点应该从其他节点那里获得更多的资源。然而，在公共成本方面，贪婪节点所获得的资源分配模式并不总是达到“社会最优”状态。我们准备了四个测试问题，其中包括一个棘手的问题，在这个问题中，贪婪的模式变得“对社会不利”，并调查RCON模型如何处理这些问题。通过比较振荡模式的问题求解性能，我们发现存在一些振荡模式，这些模式往往不会陷入贪婪模式，而是得到了社会有利的模式。
A single-celled amoeboid organism, the true slime moldPhysarum polycephalum, exhibits rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. The authors previously created a biocomputer that incorporates the organism as a computing substrate to search for solutions to combinatorial optimization problems. With the assistance of optical feedback to implement a recurrent neural network model, the organism changes its shape by alternately growing and withdrawing its photosensitive branches so that its body area can be maximized and the risk of being illuminated can be minimized. In this way, the organism succeeded in finding the optimal solution to the four-city traveling salesman problem with a high probability. However, it remains unclear how the organism collects, stores, and compares information on light stimuli using the oscillatory dynamics. To study these points, we formulate an ordinary differential equation model of the amoeba-based neurocomputer, considering the organism as a network of oscillators that compete for a fixed amount of intracellular resource. The model, called the “Resource-Competing Oscillator Network (RCON) model,” reproduces well the organism’s experimentally observed behavior, as it generates a number of spatiotemporal oscillation modes by keeping the total sum of the resource constant. Designing the feedback rule properly, the RCON model comes to face a problem of optimizing the allocation of the resource to its nodes. In the problem-solving process, “greedy” nodes having the highest competitiveness are supposed to take more resource out of other nodes. However, the resource allocation pattern attained by the greedy nodes cannot always achieve a “socially optimal” state in terms of the public cost. We prepare four test problems including a tricky one in which the greedy pattern becomes “socially unfavorable” and investigate how the RCON model copes with these problems. Comparing problem-solving performances of the oscillation modes, we show that there exist some modes often attain socially favorable patterns without being trapped in the greedy one.
DOI：
10.1016/j.physa.2006.01.053
发表时间：
2006-04-15
影响因子：
3.3
作者：
Tero, A;Kobayashi, R;Nakagaki, T
通讯作者：
Nakagaki, T
DOI：
10.1016/s0303-2647(03)00085-6
发表时间：
2003-10
期刊：
Bio Systems
影响因子：
--
作者：
M. Aono;Y. Gunji
通讯作者：
M. Aono;Y. Gunji
DOI：
10.1016/j.biosystems.2006.09.016
发表时间：
2007-02
期刊：
Bio Systems
影响因子：
--
作者：
S. Tsuda;Klaus-Peter Zauner;Y. Gunji
通讯作者：
S. Tsuda;Klaus-Peter Zauner;Y. Gunji