Computational Modeling of Neural Networks of the Human Brain

AbstractRecent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here we explore one such bias, namely the bias to perceive animacy, and used the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (how an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow-mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were strongly biased towards object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, this bias interfered with proper object identification, such as failing to signal that a cow-mug is a mug. The bias in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to veridically represent objects when visual appearance is dissociated from animacy, probably due to a biased processing of visual features typical of animate objects.

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Computational Models in Developmental Psychology

The Oxford Handbook of Developmental Psychology, Vol. 1 ◽

10.1093/oxfordhb/9780199958450.013.0017 ◽

2013 ◽

pp. 476-504

Author(s):

Thomas R. Shultz

Keyword(s):

Neural Networks ◽

Computational Modeling ◽

Computational Models ◽

Developmental Psychology ◽

Probability Distributions ◽

Network Models ◽

Developmental Theory ◽

Negative Evidence ◽

Developmental Robotics ◽

Neural Network Models

Computational modeling implements developmental theory in a precise manner, allowing generation, explanation, integration, and prediction. Several modeling techniques are applied to development: symbolic rules, neural networks, dynamic systems, Bayesian processing of probability distributions, developmental robotics, and mathematical analysis. The relative strengths and weaknesses of each approach are identified and examples of each technique are described. Ways in which computational modeling contributes to developmental issues are documented. A probabilistic model of the vocabulary spurt shows that various psychological explanations for it are unnecessary. Constructive neural networks clarify the distinction between learning and development and show how it is possible to escape Fodor’s paradox. Connectionist modeling reveals different versions of innateness and how learning and evolution might interact. Agent-based models analyze the basic principles of evolution in a testable, experimental fashion that generates complete evolutionary records. Challenges posed by stimulus poverty and lack of negative examples are explored in neural-network models that learn morphology or syntax probabilistically from indirect negative evidence.

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Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions

Neurocomputing ◽

10.1016/j.neucom.2020.01.050 ◽

2020 ◽

Vol 416 ◽

pp. 38-44

Author(s):

Emmanouil Giannakakis ◽

Cheol E. Han ◽

Bernd Weber ◽

Frances Hutchings ◽

Marcus Kaiser

Keyword(s):

Neural Networks ◽

Synaptic Plasticity ◽

Human Brain ◽

Brain Regions ◽

Long Term Behavior

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Combating fuzziness with computational modeling

Behavioral and Brain Sciences ◽

10.1017/s0140525x03460020 ◽

2003 ◽

Vol 26 (1) ◽

pp. 107-108

Author(s):

L. M. Talamini ◽

M. Meeter ◽

J. M. J. Murre

Keyword(s):

Neural Networks ◽

Computational Modeling ◽

Brain Regions ◽

Psychological Traits ◽

Detailed Mechanism ◽

Psychological Dysfunction ◽

Detailed Simulation ◽

Biological Abnormality

AbstractPhillips & Silverstein's ambitious link between receptor abnormalities and the symptoms of schizophrenia involves a certain amount of fuzziness: No detailed mechanism is suggested through which the proposed abnormality would lead to psychological traits. We propose that detailed simulation of brain regions, using model neural networks, can aid in understanding the relation between biological abnormality and psychological dysfunction in schizophrenia.

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Use of Neural Networks for Modelling of Passenger Dynamics in Airport Terminal Environment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.708.107 ◽

2014 ◽

Vol 708 ◽

pp. 107-112

Author(s):

Pavlína Hlavsová ◽

Jaromír Široký

Keyword(s):

Neural Networks ◽

Human Brain ◽

Flow Control ◽

Modelling And Simulation ◽

Mathematics Methods ◽

Flow Modelling ◽

Passenger Flow ◽

Airport Terminal ◽

Central Nervous Systems ◽

Modern Mathematics

Neural networks are methods inspired by animals´ central nervous systems, particularly by the human brain. As one of the modern mathematics methods, neural networks have been used to solve a wide variety of both practical and theoretical tasks. The aim of this paper is to illustrate the use of neural networks for modelling of passenger dynamics in the airport terminal environment. This model could be used for passenger flow control, since for the management to be appropriate it should involve passenger dynamics prediction for effective and accurate passenger flow modelling and simulation.

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Efficient Luminosity Enhancement in Human Brain Images using Pulse-Coupled Neural Networks

Computación y Sistemas ◽

10.13053/cys-24-1-3187 ◽

2020 ◽

Vol 24 (1) ◽

Author(s):

Kevin S. Aguilar Domínguez ◽

Manuel Mejía Lavalle ◽

Juan Humberto Sossa Azuela

Keyword(s):

Neural Networks ◽

Human Brain ◽

Brain Images ◽

Coupled Neural Networks ◽

Pulse Coupled Neural Networks

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