scholarly journals Collective response to perturbations in a data-driven fish school model

2015 ◽  
Vol 12 (104) ◽  
pp. 20141362 ◽  
Author(s):  
Daniel S. Calovi ◽  
Ugo Lopez ◽  
Paul Schuhmacher ◽  
Hugues Chaté ◽  
Clément Sire ◽  
...  
Keyword(s):  
Social Interactions ◽  
Data Driven ◽  
Collective State ◽  
Fish Schools ◽  
Fish School ◽  
Intrinsic Fluctuations ◽  
School Model ◽  
Main Population ◽  
Collective States

Fish schools are able to display a rich variety of collective states and behavioural responses when they are confronted by threats. However, a school's response to perturbations may be different depending on the nature of its collective state. Here we use a previously developed data-driven fish school model to investigate how the school responds to perturbations depending on its different collective states, we measure its susceptibility to such perturbations, and exploit its relation with the intrinsic fluctuations in the school. In particular, we study how a single or a small number of perturbing individuals whose attraction and alignment parameters are different from those of the main population affect the long-term behaviour of a school. We find that the responsiveness of the school to the perturbations is maximum near the transition region between milling and schooling states where the school exhibits multistability and regularly shifts between these two states. It is also in this region that the susceptibility, and hence the fluctuations, of the polarization order parameter is maximal. We also find that a significant school's response to a perturbation only happens below a certain threshold of the noise to social interactions ratio.

scholarly journals The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model

2021 ◽  
Author(s):  
Weijia Wang ◽  
Ramon Escobedo ◽  
Stephane Sanchez ◽  
Clement Sire ◽  
Zhangang Han ◽  
...  
Keyword(s):  
Social Interactions ◽  
Group Cohesion ◽  
Cognitive Factors ◽  
Data Driven ◽  
Minimal Level ◽  
Interaction Range ◽  
Fish School ◽  
Collective Movements ◽  
School Model ◽  
The Impact

In moving animal groups, social interactions play a key role in the ability of individuals to achieve coordinated motion. However, a large number of environmental and cognitive factors are able to modulate the expression of these interactions and the characteristics of the collective movements that result from these interactions. Here, we use a data-driven fish school model (Calovi et al., 2018; Lei et al., 2020) to quantitatively investigate the impact of perceptual and cognitive factors on coordination and collective swimming patterns. The model describes the interactions involved in the coordination of burst-and-coast swimming in groups of Hemigrammus rhodostomus. We perform a comprehensive investigation of the respective impacts of two interactions strategies between fish based on the selection of the most or the two most influential neighbors, of the range and intensity of social interactions, of the intensity of individual random behavioral fluctuations, and of the group size, on the ability of groups of fish to coordinate their movements. We find that fish are able to coordinate their movements when they interact with their most or two most influential neighbors, provided that a minimal level of attraction between fish exist to maintain group cohesion. A minimal level of alignment is also required to allow the formation of schooling and milling. However, increasing the strength of social interactions does not necessarily enhance group cohesion and coordination. When attraction and alignment strengths are too high, or when the heading random fluctuations are too large, schooling and milling can no longer be maintained and the school switches to a swarming phase. Increasing the interaction range between fish has a similar impact on collective dynamics as increasing the strengths of attraction and alignment. Finally, we find that coordination and schooling occurs for a wider range of attraction and alignment strength in small group sizes.

scholarly journals Swarming, schooling, milling: phase diagram of a data-driven fish school model

2014 ◽  
Vol 16 (1) ◽  
pp. 015026 ◽  
Author(s):  
Daniel S Calovi ◽  
Ugo Lopez ◽  
Sandrine Ngo ◽  
Clément Sire ◽  
Hugues Chaté ◽  
...  
Keyword(s):  
Phase Diagram ◽  
Data Driven ◽  
Fish School ◽  
School Model

35 Social Contact Mode and 15-Year Episodic Memory Trajectories in Older Adults with and Without Hearing Loss: the Elsa Study

2021 ◽  
Vol 50 (Supplement_1) ◽  
pp. i7-i11
Author(s):  
S Rafnsson ◽  
A Maharani ◽  
G Tampubolon
Keyword(s):  
Older Adults ◽  
Hearing Loss ◽  
Social Network ◽  
Episodic Memory ◽  
Social Interactions ◽  
Social Contact ◽  
Sensitivity Analyses ◽  
Impaired Hearing ◽  
Contact Mode

Abstract Introduction Frequent social contact benefits cognition in later life although evidence is lacking on the potential importance of the modes chosen by older adults for interacting with others in their social network. Method 11,513 participants in the English Longitudinal Study of Ageing (ELSA) provided baseline information on hearing status and social contact mode and frequency of use. Multilevel growth curve models compared episodic memory (immediate and delayed recall) at baseline and long-term in participants who interacted frequently (offline only or offline and online combined), compared to infrequently, with others in their social network. Results Frequent offline (β = 0.29; p < 0.05) and combined offline and online (β = 0.76; p < 0.001) social interactions predicted better episodic memory after adjustment for multiple confounding factors. We observed positive long-term influences of combined offline and online interactions on memory in participants without hearing loss (β = 0.48, p = 0.001) but not of strictly offline interactions (β = 0.00, p = 0.970). In those with impaired hearing, long-term memory was positively influenced by both modes of engagement (offline only: β = 0.93, p < 0.001; combined online and offline: β = 1.47, p < 0.001). Sensitivity analyses confirmed the robustness of these findings. Conclusion Supplementing conventional social interactions with online communication modes may help older adults, especially those living with hearing loss, sustain, and benefit cognitively from, personal relationships.

Brains that Fire Together Wire Together: Interbrain Plasticity Underlies Learning in Social Interactions

2021 ◽  
pp. 107385842199668
Author(s):  
Simone G. Shamay-Tsoory
Keyword(s):  
Social Interactions ◽  
Brain Structures ◽  
Facilitation Effect ◽  
New Approach ◽  
Short And Long Term ◽  
Long Term Experience

Social interactions are powerful determinants of learning. Yet the field of neuroplasticity is deeply rooted in probing changes occurring in synapses, brain structures, and networks within an individual brain. Here I synthesize disparate findings on network neuroplasticity and mechanisms of social interactions to propose a new approach for understanding interaction-based learning that focuses on the dynamics of interbrain coupling. I argue that the facilitation effect of social interactions on learning may be explained by interbrain plasticity, defined here as the short- and long-term experience-dependent changes in interbrain coupling. The interbrain plasticity approach may radically change our understanding of how we learn in social interactions.

scholarly journals Dominance relationships and coalitionary aggression against conspecifics in female carrion crows

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Benedikt Holtmann ◽  
Julia Buskas ◽  
Matthew Steele ◽  
Kristaps Solokovskis ◽  
Jochen B. W. Wolf
Keyword(s):  
Social Interactions ◽  
Social Environment ◽  
Social Rank ◽  
Social Benefits ◽  
Same Sex ◽  
The Third ◽  
Dominance Relationships ◽  
Dominant Individual ◽  
The Individual

Abstract Cooperation is a prevailing feature of many animal systems. Coalitionary aggression, where a group of individuals engages in coordinated behaviour to the detriment of conspecific targets, is a form of cooperation involving complex social interactions. To date, evidence has been dominated by studies in humans and other primates with a clear bias towards studies of male-male coalitions. We here characterize coalitionary aggression behaviour in a group of female carrion crows consisting of recruitment, coordinated chase, and attack. The individual of highest social rank liaised with the second most dominant individual to engage in coordinated chase and attack of a lower ranked crow on several occasions. Despite active intervention by the third most highly ranked individual opposing the offenders, the attack finally resulted in the death of the victim. All individuals were unrelated, of the same sex, and naïve to the behaviour excluding kinship, reproduction, and social learning as possible drivers. Instead, the coalition may reflect a strategy of the dominant individual to secure long-term social benefits. Overall, the study provides evidence that members of the crow family engage in coordinated alliances directed against conspecifics as a possible means to manipulate their social environment.

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

scholarly journals Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

2021 ◽  
Author(s):  
Andras Ecker ◽  
Bence Bagi ◽  
Eszter Vertes ◽  
Orsolya Steinbach-Nemeth ◽  
Maria Rita Karlocai ◽  
...  
Keyword(s):  
Place Cells ◽  
Data Driven ◽  
Long Term Memory ◽  
Term Memory ◽  
Cellular Adaptation ◽  
Sharp Wave ◽  
Synaptic Interactions ◽  
Area Ca3 ◽  
Reversed Order

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated ("replayed"), either in the same or reversed order, during bursts of activity (sharp wave-ripples; SWRs) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. We sought to identify the cellular and network mechanisms of SWRs and replay by constructing and simulating a data-driven model of area CA3 of the hippocampus. Our results show that the structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics.

scholarly journals Very early identification of a bimodal frictional behavior during the post-seismic phase of the 2015 M<sub><i>w</i></sub>8.3 Illapel, Chile, earthquake

2021 ◽  
Author(s):  
Cedric Twardzik ◽  
Mathilde Vergnolle ◽  
Anthony Sladen ◽  
Louisa L. H. Tsang
Keyword(s):  
Template Matching ◽  
State Of The Art ◽  
Data Driven ◽  
Spatiotemporal Evolution ◽  
Seismic Slip ◽  
The North ◽  
Chile Earthquake ◽  
Gnss Data ◽  
Matching Techniques

Abstract. It is well-established that the post-seismic slip results from the combined contribution of seismic slip and aseismic slip. However, the partitioning between these two modes of slip remains unclear due to the difficulty to infer detailed and robust descriptions of how both evolve in space and time. This is particularly true just after a mainshock when both processes are expected to be the strongest. Using state-of-the-art sub-daily processing of GNSS data, along with dense catalogs of aftershocks obtained from template-matching techniques, we unravel the spatiotemporal evolution of post-seismic slip and aftershocks over the first 12 hours following the 2015 Mw8.3 Illapel, Chile, earthquake. We show that the very early post-seismic activity occurs over two regions with distinct behaviors. To the north, post-seismic slip appears to be purely aseismic and precedes the occurrence of late aftershocks. To the south, aftershocks are the primary cause of the post-seismic slip. We suggest that this difference in behavior could be inferred only few hours after the mainshock, and thus could contribute to a more data-driven forecasts of long-term aftershocks.

scholarly journals Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation (Preprint)

2021 ◽  
Author(s):  
Xingzhi Sun ◽  
Yong Mong Bee ◽  
Shao Wei Lam ◽  
Zhuo Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Reinforcement Learning ◽  
Blood Lipids ◽  
Diabetes Complications ◽  
Data Driven ◽  
Lipid Lowering ◽  
Treatment Recommendation

BACKGROUND Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

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