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Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

Pragathi P. Balasubramani, Rubén Moreno-Bote, Benjamin Y. Hayden
2018 Frontiers in Computational Neuroscience  
Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological  ...  The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions.  ...  We suspect the same is true for neural responses as well. The goal of the present study is to delineate some basic principles of economic choice in distributed systems.  ... 
doi:10.3389/fncom.2018.00022 pmid:29643773 pmcid:PMC5882864 fatcat:s6dtecis3fcdhe3mcupogg7pli

Neurocognitive mechanisms underlying value-based decision-making: from core values to economic value

Tobias Brosch, David Sander
2013 Frontiers in Human Neuroscience  
Based on a review of recent neuroimaging studies investigating the neural representation of core values and their interactions with neural systems representing economic value, we outline a common framework  ...  candidate gets our vote, we choose the option that has more value to us.  ...  The individual choices (or preferences) are then used to derive a measure of economic value, which is used as a parametric regressor to identify brain regions that show systematic activation changes as  ... 
doi:10.3389/fnhum.2013.00398 pmid:23898252 pmcid:PMC3721023 fatcat:v6xhrjst6raufmpc752c4gmyjm

Neurocomputational models of altruistic decision‐making and social motives: Advances, pitfalls, and future directions

Anita Tusche, Lisa M. Bas
2021 Wiley Interdisciplinary Reviews: Cognitive Science  
Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism.  ...  We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations.  ...  There is a trend in neuroimaging research to move away from narrow localization towards analyzing distributed brain networks.  ... 
doi:10.1002/wcs.1571 pmid:34340256 pmcid:PMC9286344 fatcat:jbdr7pfsbjfbtijnufcuuye4lu

Boundary Loss for Remote Sensing Imagery Semantic Segmentation [article]

Alexey Bokhovkin, Evgeny Burnaev
2019 arXiv   pre-print
Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data.  ...  We can use the loss function with any neural network for binary segmentation.  ...  Let us denote by y pd a binary map for an arbitrary class c for some image I, predicted by a neural network, y gt a ground truth map for the same class and the same image.  ... 
arXiv:1905.07852v1 fatcat:7izb2qcb2rfd5cjpsdmsd7knoe

Choice and Process: Theory Ahead of Measurement [chapter]

JESS BENHABIB, ALBERTO BISIN
2008 The Foundations of Positive and Normative Economics  
Are models and data on choice processes useful as a complement to revealed preferences in decision theory? We answer this methodological question in the affirmative.  ...  We illustrate our arguments by means of examples from inter-temporal decision theory.  ...  To illustrate this argument it is useful to introduce a simple formal example.  ... 
doi:10.1093/acprof:oso/9780195328318.003.0014 fatcat:nqixxhpvq5dmrbfvw6wlit4rfi

Using Data-Mining Technique for Census Analysis to Give GeoSpatial Distribution of Nigeria

2013 IOSR Journal of Computer Engineering  
Decision tree algorithm was used to predict some basic attributes of population in the census database. Structured System Analysis and Design Methodology were used.  ...  This paper is an effort towards harnessing the power of data-mining technique to develop mining model applicable to the analysis of census data that could uncover some hidden patterns to get their geo-spatial  ...  One method of deriving a single prediction (for new observations) is to use all trees found in the different samples, and to apply some simple voting.  ... 
doi:10.9790/0661-1420105 fatcat:2hdzige7ofgpndanx5ta637juu

Curvelet Based Multiresolution Analysis of Graph Neural Networks

Bharat Bhosale
2014 International Journal of Applied Physics and Mathematics  
The ANN are aimed at modeling the organization principles of central neural  ...  The Signal-to-Noise Ratio and Root Mean Square Error are used as metrics to evaluate the quality of the separated signals. the field of neuroscience such as study of models of neural networks, anatomical  ...  Graph Neural Networks The neural networks can be represented by graphs showing the computational elements, neurons of the network, called as Graph neural network (GNN), which can be used to process structured  ... 
doi:10.7763/ijapm.2014.v4.304 fatcat:74m5ue53ebahdi57izusgmffbq

A novel fusion Python application of data mining techniques to evaluate airborne magnetic datasets [article]

John Stephen Kayode, Yusri Yusup
2020 arXiv   pre-print
The DMT facilitated the determination of depths to these subsurface geological source rock features with a maximum depth of approximately 1.277 km using a 3x3 window size to map the concealed features  ...  A novel fusion python application of data mining techniques (DMT) was designed and implemented to locate, identify, and delineate the subsurface structural pattern (SSP) of source rocks for the features  ...  COMPETING OF INTERESTS The authors declare that they have no known competing financial interests or personal relationships that have or could be perceived to have influenced the work reported in this article  ... 
arXiv:2006.07236v1 fatcat:hr6zmak2lzemthk3vqqw64vsfa

Economic Choice as an Untangling of Options into Actions

Seng Bum Michael Yoo, Benjamin Yost Hayden
2018 Neuron  
We propose that economic choice can be understood as a gradual transformation from domain of options to one of the actions.  ...  From this viewpoint, choice results from the accumulated effect of repetitions of simple computations.  ...  The funder had no role in study design, decision to publish, or preparation of the manuscript.  ... 
doi:10.1016/j.neuron.2018.06.038 pmid:30092213 pmcid:PMC6280664 fatcat:wbw2omndmndpxbiltekod6olrm

Learning pattern classification-a survey

S.R. Kulkarni, G. Lugosi, S.S. Venkatesh
1998 IEEE Transactions on Information Theory  
The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.  ...  Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik-Chervonenkis theory, and neural networks.  ...  Therefore, to be able to determine the size of a neural network to be used, it is important to study its VC dimension.  ... 
doi:10.1109/18.720536 fatcat:pboyft5ze5gwphln5bpglatbam

NeuroEconomics: An overview from an economic perspective

P. Kenning, H. Plassmann
2005 Brain Research Bulletin  
This paper aims to provide an overview of the current state of neuroeconomic research by giving a brief description of the concept of neuroeconomics, outlining methods commonly used and describing current  ...  The key idea of this approach is to employ recent neuroscientific methods in order to analyze economically relevant brain processes.  ...  Last but not least, we would like to thank Brigitte Scho and Jörg Niessing.  ... 
doi:10.1016/j.brainresbull.2005.07.006 pmid:16216680 fatcat:sbo7pogi2ncgxkarsym6w7xgyu

Generative Models for Network Neuroscience: Prospects and Promise [article]

Richard F. Betzel, Danielle S. Bassett
2017 arXiv   pre-print
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and to identify principles with which to understand them.  ...  We begin with a primer on network generative models, with a discussion of compressibility and predictability, utility in intuiting mechanisms, and a short history on their use in network science broadly  ...  -1626008).The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.  ... 
arXiv:1708.07958v1 fatcat:mgugkgztm5dw5glg22bsutrtwa

On the conditions for integrating deep learning into the study of visual politics

Matteo Magnani, Alexandra Segerberg
2021 13th ACM Web Science Conference 2021  
On the one hand, the complexity of visual political themes requires a more substantial human involvement if compared with other applications of deep neural networks.  ...  the delineation of the object of analysis, to data collection, to the interpretation and communication of results.  ...  Early work addressing the use of deep neural networks in the study of visual politics has focused on cases where simple labels are available and are the object of the study.  ... 
doi:10.1145/3447535.3462511 fatcat:paay5vpv35ggnoo347njhw3ciu

Generative models for network neuroscience: prospects and promise

Richard F. Betzel, Danielle S. Bassett
2017 Journal of the Royal Society Interface  
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them.  ...  We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science  ...  The authors thank Lia Papadopoulos and Evelyn Tang for helpful comments on earlier versions of this manuscript.  ... 
doi:10.1098/rsif.2017.0623 pmid:29187640 pmcid:PMC5721166 fatcat:b6hdzyjjfzhmddzcuwivbxg2we

Neuroeconomics: Infeasible and Underdetermined

Robert McMaster, Marco Novarese
2016 Journal of Economic Issues  
Advocates of neuroeconomics claim to offer the prospect of creating a "unified behavioral theory" by drawing upon the techniques of neuroscience and psychology and combining them with economic theory.  ...  Institutional economists should be cautious of neuroeconomists' zeal as they appear to over-interpret experimental findings and, therefore, it may provide a false prospectus seeking to reinforce the nostrums  ...  This means that we will eventually be able to replace the simple mathematical ideas that have been used in economics with more neurally-detailed descriptions.  ... 
doi:10.1080/00213624.2016.1249745 fatcat:6b33jrub4jhbni6khtdzlpwfbi
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