scholarly journals Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Thibault Honegger ◽  
Moritz I. Thielen ◽  
Soheil Feizi ◽  
Neville E. Sanjana ◽  
Joel Voldman
Keyword(s):  
Neural Networks ◽  
Structure Function ◽  
Population Based ◽  
Neurite Guidance ◽  
Complex Population

On an Application of Graph Neural Networks in Population-Based SHM

2021 ◽  
pp. 47-63
Author(s):  
G. Tsialiamanis ◽  
C. Mylonas ◽  
E. N. Chatzi ◽  
D. J. Wagg ◽  
N. Dervilis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Population Based ◽  
Graph Neural Networks

Précis of Neural organization: Structure, function, and dynamics

2000 ◽  
Vol 23 (4) ◽  
pp. 513-533 ◽  
Author(s):  
Michael A. Arbib ◽  
Péter Érdi
Keyword(s):  
Neural Networks ◽  
Structure Function ◽  
Organization Structure ◽  
Spatial Relations ◽  
Brain Regions ◽  
Neural Data ◽  
Cellular Processes ◽  
Neural Organization ◽  
Dynamic System Theory

Neural organization: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) Structure: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) Function: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) Dynamics: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.

scholarly journals Structure-Function Discrepancy: Inhomogeneity and Delays in Synchronized Neural Networks

2014 ◽  
Vol 10 (7) ◽  
pp. e1003736 ◽  
Author(s):  
Robert Ton ◽  
Gustavo Deco ◽  
Andreas Daffertshofer
Keyword(s):  
Neural Networks ◽  
Structure Function

scholarly journals Probing the structure-function relationship with neural networks constructed by solving a system of linear equations

2020 ◽  
Author(s):  
Camilo J. Mininni ◽  
B. Silvano Zanutto
Keyword(s):  
Neural Networks ◽  
Structure Function ◽  
Brain Function ◽  
Linear Equations ◽  
Optimization Algorithms ◽  
State Transitions ◽  
System Of Linear Equations ◽  
Network Parameters ◽  
Structure Function Relationship ◽  
Function Relationship

AbstractNeural network models are an invaluable tool to understand brain function, since they allow to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate the changes and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph followed by an agent when solving a given behavioural task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in such a way that system consistency in guarantee. This allows to uncouple the activity features of the model, like its neurons firing rate and correlation, from the connectivity features and from the task-solving algorithm implemented by the network, allowing to fit these three levels separately. We employed the method to probe the structure-function relationship in a stimuli sequence memory task, finding solution networks where commonly employed optimization algorithms failed. The constructed networks showed reciprocity and correlated firing patterns that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.

scholarly journals Parallel Pbil Applied to Power System Controller Design

2013 ◽  
Vol 3 (3) ◽  
pp. 215-223 ◽  
Author(s):  
Komla Folly
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power System ◽  
Incremental Learning ◽  
Controller Design ◽  
Population Based ◽  
Loss Of Diversity ◽  
System Controller ◽  
Two Populations ◽  
Better Than

Abstract Population-Based Incremental Learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. PBIL has recently received increasing attention in various engineering fields due to its effectiveness, easy implementation and robustness. Despite these strengths, it was reported in the last few years that PBIL suffers from issues of loss of diversity in the population. To deal with this shortcoming, this paper uses parallel PBIL based on multi-population. In parallel PBIL, two populations are used where both probability vectors (PVs) are initialized to 0.5. It is believed that by introducing two populations, the diversity in the population can be increased and better results can be obtained. The approach is applied to power system controller design. Simulations results show that the parallel PBIL approach performs better than the standard PBIL and is as effective as another diversity increasing PBIL called adaptive PBIL

scholarly journals A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks—the Cohort of Screen-Aged Women (CSAW)

2019 ◽  
Vol 33 (2) ◽  
pp. 408-413 ◽  
Author(s):  
Karin Dembrower ◽  
Peter Lindholm ◽  
Fredrik Strand
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
High Volume ◽  
Population Based ◽  
Outcome Data ◽  
Ethical Review ◽  
Cancer Center ◽  
Lymph Node Status ◽  
Incident Cancer

Abstract For AI researchers, access to a large and well-curated dataset is crucial. Working in the field of breast radiology, our aim was to develop a high-quality platform that can be used for evaluation of networks aiming to predict breast cancer risk, estimate mammographic sensitivity, and detect tumors. Our dataset, Cohort of Screen-Aged Women (CSAW), is a population-based cohort of all women 40 to 74 years of age invited to screening in the Stockholm region, Sweden, between 2008 and 2015. All women were invited to mammography screening every 18 to 24 months free of charge. Images were collected from the PACS of the three breast centers that completely cover the region. DICOM metadata were collected together with the images. Screening decisions and clinical outcome data were collected by linkage to the regional cancer center registers. Incident cancer cases, from one center, were pixel-level annotated by a radiologist. A separate subset for efficient evaluation of external networks was defined for the uptake area of one center. The collection and use of the dataset for the purpose of AI research has been approved by the Ethical Review Board. CSAW included 499,807 women invited to screening between 2008 and 2015 with a total of 1,182,733 completed screening examinations. Around 2 million mammography images have currently been collected, including all images for women who developed breast cancer. There were 10,582 women diagnosed with breast cancer; for 8463, it was their first breast cancer. Clinical data include biopsy-verified breast cancer diagnoses, histological origin, tumor size, lymph node status, Elston grade, and receptor status. One thousand eight hundred ninety-one images of 898 women had tumors pixel level annotated including any tumor signs in the prior negative screening mammogram. Our dataset has already been used for evaluation by several research groups. We have defined a high-volume platform for training and evaluation of deep neural networks in the domain of mammographic imaging.

Sign in / Sign up

Export Citation Format

Share Document