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Hokkaido University Collection of Scholarly and Academic Papers >
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Study on Identification of Leader and Follower Agents and its Interaction Domain from Trajectories in a Collectively Moving Colony
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| Title: | Study on Identification of Leader and Follower Agents and its Interaction Domain from Trajectories in a Collectively Moving Colony |
| Other Titles: | 協同的コロニーの軌道データによる先導・従エージェントとそれらの相互作⽤領域の同定に関する研究 |
| Authors: | Basak, Udoy Sankar Browse this author |
| Keywords: | Collective motion | | Causality | | Leader-follower | | Cross-correlation | | Transfer entropy | | Vicsek model | | Interaction radius | | cAMP | | PIV. |
| Issue Date: | 25-Mar-2021 |
| Publisher: | Hokkaido University |
| Abstract: | Systems composed of locally interacting particles or agents such as birds, fish, and cells
show spontaneous, spatiotemporal, collective behaviors as a whole. Exploration of the
underlying mechanisms and principles of such coordination of agents has been made in
experimental, and theoretical studies. In many systems, it has been proved that the
presence of influential individuals, known as `leaders', are responsible for the collective
motion of agents. These leaders control the movement of the whole community.
In some cases, e.g., fish shoal, MDCK epithelial cells, the relative position of the
agents helps to identify the leader. But in many cases where agents do not move in the
same direction, e.g., Dictyostelium Discoideum, the relative position does not help to
identify leaders. Hence identifying leader agents in a collectively moving community is a
perplexing work.
Since the follower's movement is regulated by the leader agents, hence there is a correlation
between the movement of leader and follower agents at a certain time lag. Consequently,
cross-correlation has been used in identifying leader and follower agents. But
its performance is questionable in non-linear systems. Transfer entropy, an information-theoretic
measure, is capable of capturing non-linear interaction between agents, hence
it has been used to identify leader agents.
The effectiveness of TE in identifying leader agents has been tested in a Dictyostelium
discoideum colony. It has been found that the result obtained using TE is almost identical
to the expert's result.
The Vicsek model (VM) often studied as a metaphor for collectively moving animals
is employed. A modified version of the VM has helped us to investigate the classification
performance of CC and TE. It has been found that TE outperforms CC. Different model
parameters have been varied to check their effect on classification scores.
An information-theoretic scheme is proposed to estimate the underlying domain of
interactions and the timescale of the interactions for many-particle systems. Based on
ensemble data of trajectories of the model system, it is shown that using the interaction
domain significantly improves the performance of classification of leaders and followers
compared to the approach without utilizing knowledge of the domain. Given an interaction
timescale estimated from an ensemble of trajectories, the first derivative of transfer
entropy averaged over the ensemble with respect to the cut-off distance is presented to
serve as an indicator to infer the interaction domain. It is shown transfer entropy is superior
to infer the interaction radius compared to cross-correlation, hence resulting in a
higher performance to infer leader-follower relationship. Effects of noise size exerted from
the environment, and the ratio of the numbers of leader and follower on the classification
performance is also discussed.
Later it was found that the `minimum derivative' scheme is dependent on how transfer
entropy can be estimated so that it takes into account enough statistics of interacting
particles, and positions and numbers of the minimum of the derivative of average transfer
entropy along with the cutoff distance λ may also be subject to the extent of external
noise and time length of trajectories. The author has scrutinized how the prediction
performance in capturing the underlying interaction domain depends on the size of noise
and time length of the trajectory data. An alternative scheme has been proposed which is
expected to be stable against noises and time length, that relies on the degree of convexity
at coarse-grained scale in the derivative of average transfer entropy along with the cutoff
distance, and time variance of underlying interaction radius of particles. |
| Description: | 担当：理学部図書室 |
| Conffering University: | 北海道大学 |
| Degree Report Number: | 甲第14389号 |
| Degree Level: | 博士 |
| Degree Discipline: | 生命科学 |
| Examination Committee Members: | (主査) 教授 小松崎 民樹（電子科学研究所）, 教授 芳賀 永, 教授 グン 剣萍 |
| Degree Affiliation: | 生命科学院（生命科学専攻） |
| Type: | theses (doctoral) |
| URI: | http://hdl.handle.net/2115/81984 |
| Appears in Collections: | 課程博士 (Doctorate by way of Advanced Course) > 生命科学院(Graduate School of Life Science)
学位論文 (Theses) > 博士 （生命科学）
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