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Journal articles on the topic 'Architecture and Neuroscience'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Eberhard, John P. "Applying Neuroscience to Architecture." Neuron 62, no. 6 (2009): 753–56. http://dx.doi.org/10.1016/j.neuron.2009.06.001.

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Coburn, Alex, Oshin Vartanian, and Anjan Chatterjee. "Buildings, Beauty, and the Brain: A Neuroscience of Architectural Experience." Journal of Cognitive Neuroscience 29, no. 9 (2017): 1521–31. http://dx.doi.org/10.1162/jocn_a_01146.

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A burgeoning interest in the intersection of neuroscience and architecture promises to offer biologically inspired insights into the design of spaces. The goal of such interdisciplinary approaches to architecture is to motivate construction of environments that would contribute to peoples' flourishing in behavior, health, and well-being. We suggest that this nascent field of neuroarchitecture is at a pivotal point in which neuroscience and architecture are poised to extend to a neuroscience of architecture. In such a research program, architectural experiences themselves are the target of neur

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3

Jeffery, Kate. "Urban Architecture: A Cognitive Neuroscience Perspective." Design Journal 22, no. 6 (2019): 853–72. http://dx.doi.org/10.1080/14606925.2019.1662666.

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4

Sternberg, Esther M., and Matthew A. Wilson. "Neuroscience and Architecture: Seeking Common Ground." Cell 127, no. 2 (2006): 239–42. http://dx.doi.org/10.1016/j.cell.2006.10.012.

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Saidi, Aurora. "Architecture Vs Neuroscience: The Interpretation of Research Results in Neuroscience to Support Phenomenological Issues in Architecture." Igra ustvarjalnosti - Creativity Game 2019, no. 07 (2019): 033–37. http://dx.doi.org/10.15292/iu-cg.2019.07.033-037.

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6

Robinson, Sarah. "John Dewey and the dialogue between architecture and neuroscience." Architectural Research Quarterly 19, no. 4 (2015): 361–67. http://dx.doi.org/10.1017/s1359135515000627.

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Sustainability is the most significant force to change architecture since the breakthrough of modernism a century ago. So far the contributions of architects to this mandate have largely amounted to technological interventions. Yet the urgent call for sustainability demands going beyond merely technological solutions to modify behavioral patterns, cultural habits and even our deeply ingrained ideas about ourselves. The very notion that architecture could modify behavioral patterns, or the sedimentation of habits seems far-fetched in an epistemological framework that has drawn strict lines betw

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Martin, Andrea E. "A Compositional Neural Architecture for Language." Journal of Cognitive Neuroscience 32, no. 8 (2020): 1407–27. http://dx.doi.org/10.1162/jocn_a_01552.

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Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing int

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Maleki, Mohammad Reza, and Qader Bayzidi. "Application of Neuroscience on Architecture: The Emergence of New Trend of Neuroarchitecture." Kurdistan Journal of Applied Research 2, no. 3 (2017): 383–96. http://dx.doi.org/10.24017/science.2017.3.62.

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Understanding and knowing neuroscience and its sub-branches applications as well as their interconnections has a significant effect on science development. Simoltaneous application of neurology, psychology and architecture gain a new trend named “neuroarchitecture” or “basic nerve architecture”. This paper tries to consider this new trend through qualitative research and descriptive analysis method based on library information analysis. In this way current paper firstly deals with this subject background and neuroscience definition and then refers to entry areas of this subject to other scienc

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Whitelaw, Alison. "Introducing ANFA, the Academy of Neuroscience for Architecture." Intelligent Buildings International 5, sup1 (2013): 1–3. http://dx.doi.org/10.1080/17508975.2013.818764.

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Psujek, Sean, Jeffrey Ames, and Randall D. Beer. "Connection and Coordination: The Interplay Between Architecture and Dynamics in Evolved Model Pattern Generators." Neural Computation 18, no. 3 (2006): 729–47. http://dx.doi.org/10.1162/neco.2006.18.3.729.

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We undertake a systematic study of the role of neural architecture in shaping the dynamics of evolved model pattern generators for a walking task. First, we consider the minimum number of connections necessary to achieve high performance on this task. Next, we identify architectural motifs associated with high fitness. We then examine how high-fitness architectures differ in their ability to evolve. Finally, we demonstrate the existence of distinct parameter subgroups in some architectures and show that these subgroups are characterized by differences in neuron excitabilities and connection si

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Gepshtein, Sergei, and Joseph Snider. "Neuroscience for architecture: The evolving science of perceptual meaning." Proceedings of the National Academy of Sciences 116, no. 29 (2019): 14404–6. http://dx.doi.org/10.1073/pnas.1908868116.

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Ma, Yang, and Yu. "FSRFNet: Feature-Selective and Spatial Receptive Fields Networks." Applied Sciences 9, no. 19 (2019): 3954. http://dx.doi.org/10.3390/app9193954.

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The attention mechanism plays a crucial role in the human visual experience. In the cognitive neuroscience community, the receptive field size of visual cortical neurons is regulated by the additive effect of feature-selective and spatial attention. We propose a novel architectural unit called a “Feature-selective and Spatial Receptive Fields” (FSRF) block that implements adaptive receptive field sizes of neurons through the additive effects of feature-selective and spatial attention. We show that FSRF blocks can be inserted into the architecture of existing convolutional neural networks to fo

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13

Paterson, Mark. "Architecture of Sensation." Body & Society 23, no. 1 (2016): 3–35. http://dx.doi.org/10.1177/1357034x16662324.

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Recent social theory that stresses the ‘nonrepresentational’, the ‘more-than visual’, and the relationship between affect and sensation have tended to assume some kind of break or rupture from historical antecedents. Especially since the contributions of Crary and Jay in the 1990s, when it comes to perceiving the built environment the complexities of sensation have been partially obscured by the dominance of a static model of vision as the principal organizing modality. This article returns to some prior historical articulations of the significance of motility in perception, retracing pathways

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14

Kim, Danbee, and Adam R. Kampff. "Neuroscience Does Design: What the Brain's Architecture Can Teach Architects." Architectural Design 90, no. 6 (2020): 94–99. http://dx.doi.org/10.1002/ad.2637.

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15

Dougherty, Betsey Olenick, and Michael A. Arbib. "The evolution of neuroscience for architecture: introducing the special issue." Intelligent Buildings International 5, sup1 (2013): 4–9. http://dx.doi.org/10.1080/17508975.2013.818763.

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16

Charles, Adam S., Pierre Garrigues, and Christopher J. Rozell. "A Common Network Architecture Efficiently Implements a Variety of Sparsity-Based Inference Problems." Neural Computation 24, no. 12 (2012): 3317–39. http://dx.doi.org/10.1162/neco_a_00372.

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The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide variety of sparsity-based probabilistic inference problems proposed in the signal processing and statistics literatures can be implemented exactly in the common network architecture known as the locally competitive algorithm (LCA). Among the c

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Shahsavari, Mahyar, Jonathan Beaumont, David Thomas, and Andrew D. Brown. "POETS: A Parallel Cluster Architecture for Spiking Neural Network." International Journal of Machine Learning and Computing 11, no. 4 (2021): 281–85. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1048.

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Spiking Neural Networks (SNNs) are known as a branch of neuromorphic computing and are currently used in neuroscience applications to understand and model the biological brain. SNNs could also potentially be used in many other application domains such as classification, pattern recognition, and autonomous control. This work presents a highly-scalable hardware platform called POETS, and uses it to implement SNN on a very large number of parallel and reconfigurable FPGA-based processors. The current system consists of 48 FPGAs, providing 3072 processing cores and 49152 threads. We use this hardw

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18

Coombes, Stephen, Brent Doiron, Krešimir Josić, and Eric Shea-Brown. "Towards blueprints for network architecture, biophysical dynamics and signal transduction." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, no. 1849 (2006): 3301–18. http://dx.doi.org/10.1098/rsta.2006.1903.

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We review mathematical aspects of biophysical dynamics , signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines.

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19

Vinogradova, Evgenia I., and Evgeny V. Kilimnik. "ANALYSIS OF COLLABORATIVE RESEARCH IN ARCHITECTURE AND PSYCHOLOGY OF THE LATE TWENTIETH-EARLY TWENTY-FIRST CENTURY." Articult, no. 3 (2020): 137–48. http://dx.doi.org/10.28995/2227-6165-2020-3-137-148.

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The article analyzes the work of Western and Russian scientists, conducted in the past three decades, on the relationship of psychology and architecture. It is shown that in the West, the neuropsychological aspects of the relationship of psychology and architecture are studied thanks to modern neurobiological equipment, while in Russia there is a clear gap between the representatives of neuroscience, their technical support, and the architectural scientific community. As a result of the analysis conducted in the article, it is concluded that two research blocks can be distinguished. The first

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20

Njegovanović, Ana. "How Do We Decide? Thought Architecture Decision Making?" Financial Markets, Institutions and Risks 5, no. 2 (2021): 58–71. http://dx.doi.org/10.21272/fmir.5(2).58-71.2021.

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The study of decision-making is an intellectual discipline; mathematics, sociology, psychology, economics, political science, artificial intelligence, neuroscience and physics. Conventional decision theory tells us what choice of behavior should be made if we follow certain axioms. Scientific curiosity instructs us to reconsider beyond any area in which we have defined ourselves. We design the intertwining of brain, genetics, phylogenetics, and artificial and neural networks in financial trading to find the best combinations of parameter values in financial trading, incorporating them into ANN

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21

van der Velde, Frank, and Marc de Kamps. "Neural blackboard architectures of combinatorial structures in cognition." Behavioral and Brain Sciences 29, no. 1 (2006): 37–70. http://dx.doi.org/10.1017/s0140525x06009022.

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Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2,

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22

Gerrans, Philip, and Valerie E. Stone. "Generous or Parsimonious Cognitive Architecture? Cognitive Neuroscience and Theory of Mind." British Journal for the Philosophy of Science 59, no. 2 (2008): 121–41. http://dx.doi.org/10.1093/bjps/axm038.

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23

Piedade Ferreira, Maria da. "Playing Seriously: An Introduction to Corporeal Architecture, Neuroscience, and Performance Art." Dimensions 1, no. 1 (2021): 119–28. http://dx.doi.org/10.14361/dak-2021-0115.

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Editorial Summary With a focus on experiential qualities Maria da Piedade Ferreira distinguishes her research object from classical (rather technical) quantities such as load-bearing capacities. In her text she illustrates how she employs methods, techniques, and instruments from performance art and neurosciences to investigate the effects of spatial conditions on the human body. In doing so, she explores a mix of qualitative and quantitative approaches, namely by experimenting with emotion measurement: Qualitative research, by including methodologies which attribute measurable values to the f

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24

Torres, Gustavo, Karina Jaime, and Félix Ramos. "Brain Architecture for Visual Object Identification." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 1 (2013): 75–97. http://dx.doi.org/10.4018/jcini.2013010104.

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Visual memory identification is a key cognitive process for intelligent virtual agents living on virtual environments. This process allows the virtual agents to develop an internal representation of the environment for the posterior production of intelligent responses. There are many architectures based on memory modules for environment visual elements identification, as if they were invariant, this way of processing a visual scene is different from the one that real humans use. This document presents the description of a visual memory identification model based on current neuroscience state o

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25

van der Velde, Frank, and Marc de Kamps. "Involvement of a visual blackboard architecture in imagery." Behavioral and Brain Sciences 25, no. 2 (2002): 213–14. http://dx.doi.org/10.1017/s0140525x02530041.

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We discuss a visual blackboard architecture that could be involved in imagery. In this architecture, networks that process identity information interact with networks that process location information, in a manner that produces structural (compositional) forms of representation. Architectures of this kind can be identified in the visual cortex, but perhaps also in prefrontal cortex areas related with working memory.

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26

Gans, Carl, and Abbot S. Gaunt. "Muscle Architecture and Control Demands." Brain, Behavior and Evolution 40, no. 2-3 (1992): 70–81. http://dx.doi.org/10.1159/000113904.

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27

Albright, Thomas D., Sergei Gepshtein, and Eduardo Macagno. "Visual Neuroscience for Architecture: Seeking a New Evidence‐Based Approach to Design." Architectural Design 90, no. 6 (2020): 110–17. http://dx.doi.org/10.1002/ad.2639.

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28

Mattar, Marcelo G., Sharon L. Thompson-Schill, and Danielle S. Bassett. "The network architecture of value learning." Network Neuroscience 2, no. 2 (2018): 128–49. http://dx.doi.org/10.1162/netn_a_00021.

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Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact with one another and with other regions of the brain to support the dynamic acquisition of value-related knowledge. Here, we use a network neuroscience approach to examine how BOLD functional networks change as 20 healthy human subjects learn the values of novel visual stimuli over the course of fo

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29

Jaime, Karina, Gustavo Torres, Félix Ramos, and Gregorio Garcia-Aguilar. "A Cognitive Architecture for Visual Memory Identification." International Journal of Software Science and Computational Intelligence 6, no. 2 (2014): 63–77. http://dx.doi.org/10.4018/ijssci.2014040104.

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Memory is an important process of human behavior. In particular visual memory encode, store, and retrieve acquired knowledge about the environment. The visual memory system involves different kinds of processes, such as sensory input and short-term visual memory. The model presents a first approach for visual memory recognition that supports the three stages mentioned above. The model design is based on neuroscience results. The model consists of nodes. Each node represents a brain area that is involved in the visual memory system. The nodes run in a distributed system and send messages with v

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30

Decety, Jean, and Philip L. Jackson. "The Functional Architecture of Human Empathy." Behavioral and Cognitive Neuroscience Reviews 3, no. 2 (2004): 71–100. http://dx.doi.org/10.1177/1534582304267187.

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31

Jacobs, Robert A. "Bias/Variance Analyses of Mixtures-of-Experts Architectures." Neural Computation 9, no. 2 (1997): 369–83. http://dx.doi.org/10.1162/neco.1997.9.2.369.

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This article investigates the bias and variance of mixtures-of-experts (ME) architectures. The variance of an ME architecture can be expressed as the sum of two terms: the first term is related to the variances of the expert networks that comprise the architecture and the second term is related to the expert networks' covariances. One goal of this article is to study and quantify a number of properties of ME architectures via the metrics of bias and variance. A second goal is to clarify the relationships between this class of systems and other systems that have recently been proposed. It is sh

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32

Tonn Eisinger, Katherine R., and Anne E. West. "Transcribing Memories in Genome Architecture." Trends in Neurosciences 42, no. 9 (2019): 565–66. http://dx.doi.org/10.1016/j.tins.2019.06.002.

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33

Carlisle, Holly J., and Mary B. Kennedy. "Spine architecture and synaptic plasticity." Trends in Neurosciences 28, no. 4 (2005): 182–87. http://dx.doi.org/10.1016/j.tins.2005.01.008.

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34

Mitchison, Graeme. "Axonal trees and cortical architecture." Trends in Neurosciences 15, no. 4 (1992): 122–26. http://dx.doi.org/10.1016/0166-2236(92)90352-9.

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35

Müller, Peter, and David Rios Insua. "Issues in Bayesian Analysis of Neural Network Models." Neural Computation 10, no. 3 (1998): 749–70. http://dx.doi.org/10.1162/089976698300017737.

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Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed-architecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data-driven procedure to identify sensible architectures.

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36

Lyu, Xin, and Taigyoun Cho. "Analysis of Shape Generation Methods in Architecture from the Perspective of Cognitive Neuroscience." Journal of Basic Design & Art 20, no. 2 (2019): 93–106. http://dx.doi.org/10.47294/ksbda.20.2.8.

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37

RAIZER, KLAUS, ANDRÉ L. O. PARAENSE, and RICARDO R. GUDWIN. "A COGNITIVE ARCHITECTURE WITH INCREMENTAL LEVELS OF MACHINE CONSCIOUSNESS INSPIRED BY COGNITIVE NEUROSCIENCE." International Journal of Machine Consciousness 04, no. 02 (2012): 335–52. http://dx.doi.org/10.1142/s1793843012400197.

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38

Wang, Frank Zhigang, Leon O. Chua, Xiao Yang, et al. "Adaptive Neuromorphic Architecture (ANA)." Neural Networks 45 (September 2013): 111–16. http://dx.doi.org/10.1016/j.neunet.2013.02.009.

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39

Spratling, M. W., and M. H. Johnson. "Preintegration Lateral Inhibition Enhances Unsupervised Learning." Neural Computation 14, no. 9 (2002): 2157–79. http://dx.doi.org/10.1162/089976602320264033.

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A large and influential class of neural network architectures uses postintegration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented here in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through preintegration lateral inhibition, does provide appropriate coding properties and can b

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40

Hanczyc, Martin M., Barbara Imhof, and Andrew Adamatzky. "Living architecture: workshop report from the European Conference on Artificial Life, Lyon, France, 4 September 2017." Adaptive Behavior 26, no. 2 (2018): 85–88. http://dx.doi.org/10.1177/1059712318761518.

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Imagine evolving swarms of robots interacting and by doing so reshaping and cultivating our habitat. This habitat could be here on Earth, on a distant planet or moon, or within a self-contained spacecraft. What would these robots look like and made of what type of material? What kind of information, hardware or software? What are the architectural necessities? There are many open questions when trying to envision the future of architecture; but, in this particular workshop, the goal was not only to imagine the future but also to create it. With this particular goal in mind, the Living Architec

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41

Pektaş, Şule Taşlı. "A scientometric analysis and review of spatial cognition studies within the framework of neuroscience and architecture." Architectural Science Review 64, no. 4 (2021): 374–82. http://dx.doi.org/10.1080/00038628.2021.1910480.

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42

Zhu, Fei, Mélissa Cizeron, Zhen Qiu, et al. "Architecture of the Mouse Brain Synaptome." Neuron 99, no. 4 (2018): 781–99. http://dx.doi.org/10.1016/j.neuron.2018.07.007.

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43

Reif, Andreas, and Klaus-Peter Lesch. "Toward a molecular architecture of personality." Behavioural Brain Research 139, no. 1-2 (2003): 1–20. http://dx.doi.org/10.1016/s0166-4328(02)00267-x.

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Brecht, Michael, Bruno Preilowski, and Michael M. Merzenich. "Functional architecture of the mystacial vibrissae." Behavioural Brain Research 84, no. 1-2 (1997): 81–97. http://dx.doi.org/10.1016/s0166-4328(97)83328-1.

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Ignell, Rickard, Teun Dekker, Majid Ghaninia, and Bill S. Hansson. "Neuronal architecture of the mosquito deutocerebrum." Journal of Comparative Neurology 493, no. 2 (2005): 207–40. http://dx.doi.org/10.1002/cne.20800.

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Holt, Daphne J., Ann M. Graybiel, and Clifford B. Saper. "Neurochemical architecture of the human striatum." Journal of Comparative Neurology 384, no. 1 (1997): 1–25. http://dx.doi.org/10.1002/(sici)1096-9861(19970721)384:1<1::aid-cne1>3.0.co;2-5.

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47

Kelley, Kevin W., and Michael C. Oldham. "Transcriptional architecture of the human brain." Nature Neuroscience 18, no. 12 (2015): 1699–701. http://dx.doi.org/10.1038/nn.4178.

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48

Flaherty, AW, and AM Graybiel. "Output architecture of the primate putamen." Journal of Neuroscience 13, no. 8 (1993): 3222–37. http://dx.doi.org/10.1523/jneurosci.13-08-03222.1993.

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Linstedt, Adam D., and Regis B. Kelly. "Molecular architecture of the nerve terminal." Current Opinion in Neurobiology 1, no. 3 (1991): 382–87. http://dx.doi.org/10.1016/0959-4388(91)90057-e.

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Sokolov, E. N. "The architecture of the reflex arc." Neuroscience and Behavioral Physiology 24, no. 1 (1994): 5–11. http://dx.doi.org/10.1007/bf02355647.

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