Complexity in a brain-inspired agent-based model

Joyce, Karen E.; Laurienti, Paul J.; Hayasaka, Satoru

doi:10.1016/j.neunet.2012.05.012

Cited by 9 publications

(11 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we tested whether changes in network topology would impact the ability of the ABBM to make decisions. While targeted attack of hubs or antihubs impacted the accuracy to some degree, the average accuracy over a range of densities was still high, much higher than the accuracy of null models with randomized connectivity shown in [27]. Random failure resulted in a greater decrease in accuracy than targeted attack.…”

Section: Discussionmentioning

confidence: 83%

“…However, random networks, which cannot solve the density-classification task, are also characterized by high global efficiency. On the other hand, elementary cellular automata and other lattice-like networks with high local efficiency have been shown to be able to solve the density classification task with high accuracy [27], [30]. In these networks, nodes are clustered into well-connected groups and can share information readily, and therefore may be able to synchronize more easily.…”

Section: Discussionmentioning

confidence: 99%

“…Changes in dynamics were also evaluated by embedding a coarser form of each network into an agent-based model called the agent-based brain-inspired model (ABBM) described in [27]. An agent-based model is a collection of agents that interact with one another by following simple rules.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, positive and negative weighted links were present in the ROI networks. The process of creating the ROI networks and the mechanisms underlying the ABBM are described in full in a prior publication [27].…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack

2013

Self Cite

View full text Add to dashboard Cite

In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.

show abstract

Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack

2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…Second, the interpretation of analysis results from a fully connected network must be made cautiously. These networks are no longer comparable to sparse networks that depend on connections between clusters for information spread [59]. Given these (and other) computational and methodological challenges that weighted networks pose [7, 50, 58], binary network analysis still dominates the literature.…”

Section: Network Constructionmentioning

confidence: 99%

Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain

Simpson¹,

Bowman²,

2013

Self Cite

View full text Add to dashboard Cite

Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.

show abstract

Agent‐based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

Hunt

Kennedy

Kim

et al. 2013

WIREs Mechanisms of Disease

View full text Add to dashboard Cite

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.

show abstract

Complexity in a brain-inspired agent-based model

Cited by 9 publications

References 37 publications

The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack

The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack

Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain

Agent‐based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

Contact Info

Product

Resources

About