Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Psychological and Biological Models

Applied cognitive gerontology: Some problems, methodologies and data

Rabbitt, P. (1990) · Applied Cognitive Psychology · doi:10.1002/acp.2350040402 · Cited by 43

Networks from mutants

Bray, D., & Vasiliev, J. M. (1989) · Nature · doi:10.1038/338203a0 · Cited by 40

Are children's representations of words distributed? effects of orthographic neighbourhood size, consistency and regularity of naming

LAXON, V. J., Masterson, J., & Moran, R. (1994) · Language and Cognitive Processes · doi:10.1080/01690969408402108 · Cited by 40

Program Execution in Connectionist Networks

Roth, M. (2005) · Mind & Language · doi:10.1111/j.0268-1064.2005.00295.x · Cited by 38

A mesh optimization method using machine learning technique and variational mesh adaptation

Wu, T., Liu, X., An, W., Huang, Z., & Lyu, H. (2021) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2021.05.018 · Cited by 39

Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach

Kariri, E., Louati, H., Louati, A., & Masmoudi, F. (2023) · Applied Sciences · doi:10.3390/app13053186 · Cited by 51

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion

Cai, B., & Jiang, X. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.12.009 · Cited by 39

Inversion of lunar regolith layer thickness with CELMS data using BPNN method

Meng, Z., Xu, Y., Zheng, Y., Zhu, Y., Jia, Y., & Chen, S. (2014) · Planetary and Space Science · doi:10.1016/j.pss.2014.05.020 · Cited by 39

Influences of variable scales and activation functions on the performances of multilayer feedforward neural networks

Gao, D., & Yang, G. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(02)00120-6 · Cited by 39

Predictions of vapor pressures of aqueous desiccants for cooling applications by using artificial neural networks

Gandhidasan, P., & Mohandes, M. A. (2007) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2007.03.034 · Cited by 39

Bathymetry Determination from High Resolution Satellite Imagery Using Ensemble Learning Algorithms in Shallow Lakes: Case Study El-Burullus Lake

Mohamed, H., Negm, A. M., Zahran, M., et al. (2015) · International Journal of Environmental Science and Development · doi:10.7763/ijesd.2016.v7.787 · Cited by 39

Neurocontrol for lateral vehicle guidance

Neußer, S., Nijhuis, J. A. G., Spaanenburg, L., et al. (1993) · IEEE Micro · doi:10.1109/40.210525 · Cited by 39

On the uniformity of error propagation in software

Michael, C. C., & Jones, R. C. (2002) · doi:10.1109/cmpass.1997.613237 · Cited by 39

A Brief Review on Deep Learning Applications in Genomic Studies

Shen, X., Jiang, C., Wen, Y., Li, C., & Lu, Q. L. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.877717 · Cited by 44

Decision making using multiple models

Malhotra, M. K., Sharma, S., & Nair, S. S. (1999) · European Journal of Operational Research · doi:10.1016/s0377-2217(98)00037-x · Cited by 39

Fuzzy models for analysis of rock mass displacements due to underground mining in mountainous areas

Li, W., Mei, S., Zai, S., et al. (2005) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/j.ijrmms.2005.09.008 · Cited by 38

Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network

Hinoshita, W., Arie, H., Tani, J., Okuno, H. G., & Ogata, T. (2011) · Neural Networks · doi:10.1016/j.neunet.2010.12.006 · Cited by 37

Expert prediction, symbolic learning, and neural networks. An experiment on greyhound racing

Chen, H., Rinde, P. B., She, L., et al. (1994) · IEEE Expert · doi:10.1109/64.363260 · Cited by 38

Evaluation of constructive neural networks with cascaded architectures

Lahnajärvi, J. J. T., Lehtokangas, M. I., & Saarinen, J. P. P. (2002) · Neurocomputing · doi:10.1016/s0925-2312(01)00630-0 · Cited by 38

Models of neural timing

Miall, C. (1996) · Advances in psychology · doi:10.1016/s0166-4115(96)80053-7 · Cited by 39

Forecasting economic turning points with neural nets

Hoptroff, R., Bramson, M. J., & Hall, T. J. (2002) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1991.155201 · Cited by 38

Estimation of time-to-flashover characteristics of contaminated electrolytic surfaces using a neural network

Ghosh, P. S., Chakravorti, S., & Chatterjee, N. (1995) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/94.484308 · Cited by 38

The Cerebral Hemispheres and Bilateral Neural Nets

Cook, N. D., & Beech, A. R. (1990) · International Journal of Neuroscience · doi:10.3109/00207459009000522 · Cited by 38

An analysis of noisy RAM and neural nets

Gorse, D., & Taylor, J. G. (1989) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(89)90229-7 · Cited by 38

Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena

Freiesleben, T., König, G., Molnar, C., & Tejero-Cantero, Á. (2024) · Minds and Machines · doi:10.1007/s11023-024-09691-z · Cited by 66

What do We Know about Neonatal Cognition?

Stréri, A., de Hevia, M. D., Véronique Izard, A., & Coubart, A. (2013) · Behavioral Sciences · doi:10.3390/bs3010154 · Cited by 38

Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget

Bonelli, A. G., Vantrepotte, V., Jorge, D. S. F., et al. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112637 · Cited by 37

A hybrid wavelet–machine learning model for qanat water flow prediction

Samani, S., Vadiati, M., Delkash, M., & Bonakdari, H. (2022) · Acta Geophysica · doi:10.1007/s11600-022-00964-8 · Cited by 39

Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network

Shah, A. A., Alsayed, S. H., Abbas, H., & Al-Salloum, Y. (2011) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2011.10.038 · Cited by 37

Discrimination of soybean leaflet shape by neural networks with image input

Oide, M., & Ninomiya, S. (2000) · Computers and Electronics in Agriculture · doi:10.1016/s0168-1699(00)00136-8 · Cited by 38

Connectionism and the Philosophy of Mind: An Overview

Bechtel, W. (1991) · Studies in cognitive systems · doi:10.1007/978-94-011-3524-5_2 · Cited by 37

Automated measurement and compensation of thermally induced error maps in machine tools

Srinivasa, N., & Ziegert, J. C. (1996) · Precision Engineering · doi:10.1016/s0141-6359(96)00042-6 · Cited by 37

Segmentation and classification for mixed text/image documents using neural network

Imade, S., Tatsuta, S., & Wada, T. (2002) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.1993.395584 · Cited by 37

Application of a neural network to analyse on-line milking parlour data for the detection of clinical mastitis in dairy cows

Nielen, M., Spigt, M. H., Schukken, Y. H., Deluyker, H., Maatje, K., & Brand, A. (1995) · Preventive Veterinary Medicine · doi:10.1016/0167-5877(94)00405-8 · Cited by 36

A universal neural net with guaranteed convergence to zero system error

Chang, T.-S., & Abdel-Ghaffar, K. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.175745 · Cited by 37

Continuity theories of human language origins versus the lieberman model

Gibson, K. R. (1994) · Language & Communication · doi:10.1016/0271-5309(94)90022-1 · Cited by 37

Multisensor integration system based on fuzzy inference and neural network

Fukuda, T., Shimojima, K., Arai, F., et al. (1993) · Information Sciences · doi:10.1016/0020-0255(93)90064-s · Cited by 37

The Mind and its Stories: Narrative Universals and Human Emotion

Hogan, P. C. (2003) · doi:10.1017/cbo9780511499951 · Cited by 37

Prediction and control of profile for silicon steel strip in the whole tandem cold rolling based on PSO-BP algorithm

Han, G., Li, H., Wang, G., Liu, Y., Zhang, J., Hu, Z., You, X., & Xie, Y. (2024) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2024.04.050 · Cited by 36

Artificial intelligence and computer simulation models in critical illness

Lal, A., Pinevich, Y., Gajic, O., Herasevich, V., & Pickering, B. (2020) · World Journal of Critical Care Medicine · doi:10.5492/wjccm.v9.i2.13 · Cited by 34

A feed-forward network for input that is both categorical and quantitative

Brouwer, R. K. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00090-4 · Cited by 36

Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG

Reddy, M. R., Edenbrandt, L., Svensson, J., et al. (2003) · Computers in cardiology · doi:10.1109/cic.1992.269345 · Cited by 36

Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning

Goodfellow, I., Courville, A. C., & Bengio, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.273 · Cited by 38

A complex mapping network for phase sensitive classification

Birx, D. L., & Pipenberg, S. J. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.182703 · Cited by 36

How many potentially secreted proteins are contained in a bacterial genome?

Schneider, G. (1999) · Gene · doi:10.1016/s0378-1119(99)00310-8 · Cited by 36

Image processing and pattern recognition in ultrasonograms by backpropagation

Silverman, R. H., & Noetzel, A. S. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90009-a · Cited by 36

Towards connectionist rule-based systems

Samad (1988) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1988.23968 · Cited by 36

Connectionism Versus Symbolism in High-Level Cognition

Dyer, M. G. (1991) · Studies in cognitive systems · doi:10.1007/978-94-011-3524-5_17 · Cited by 36

Machine learning for board-level drop response of BGA packaging structure

Mao, M., Wang, W., Lu, C., et al. (2022) · Microelectronics Reliability · doi:10.1016/j.microrel.2022.114553 · Cited by 35

Towards integrating intelligence in electronic skin

Seminara, L., Pinna, L., Ibrahim, A., et al. (2015) · Mechatronics · doi:10.1016/j.mechatronics.2015.04.001 · Cited by 35

Interest Point Detector and Feature Descriptor Survey

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_6 · Cited by 37

An artificial intelligence approach to classify and analyse EEG traces

Castellaro, C., Favaro, G., Castellaro, A., Casagrande, A., Castellaro, S., Puthenparampil, D. V., & Salimbeni, C. F. (2002) · Neurophysiologie Clinique · doi:10.1016/s0987-7053(02)00302-7 · Cited by 35

A visual language compiler for information retrieval by visual reasoning

Chang, S.-K. (1990) · IEEE Transactions on Software Engineering · doi:10.1109/32.60294 · Cited by 35

Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts

Maass, W., & Turán, G. (1994) · Machine Learning · doi:10.1023/a:1022653511837 · Cited by 40

Scientific psychology: Should we bury it or praise it?

Gardner, H. (1992) · New Ideas in Psychology · doi:10.1016/0732-118x(92)90027-w · Cited by 35

Connectionism, modularity, and tacit knowledge

DAVIES, M. (1989) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/40.4.541 · Cited by 35

Nonlinear Input/Output Modeling

Pearson, R. K. (1994) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)48122-5 · Cited by 36

Advanced automation concepts for large-scale systems

Berkan, R. C., Upadhyaya, B. R., Tsoukalas, L. H., et al. (1991) · IEEE Control Systems · doi:10.1109/37.92985 · Cited by 35

Parsing Complex Sentences with Structured Connectionist Networks

Jain, A. N. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.110 · Cited by 34

3.8 Remote Sensing and GIScience in Geomorphological Mapping

Napieralski, J. A., Barr, I. D., Kamp, U., et al. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-374739-6.00050-6 · Cited by 34

A serial complexity measure of neural networks

Sipper, M. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1993.298687 · Cited by 34

Scientists at major and minor universities: mobility along the prestige continuum

Debackere, K., & Rappa, M. A. (1995) · Research Policy · doi:10.1016/0048-7333(95)98447-3 · Cited by 34

Methodological Investigations in Agent-Based Modelling

Silverman, E. (2018) · Springer eBooks · doi:10.1007/978-3-319-72408-9 · Cited by 34

A Neural Network Explanation of Posttraumatic Stress Disorder

Tryon, W. W. (1998) · Journal of Anxiety Disorders · doi:10.1016/s0887-6185(98)00021-8 · Cited by 34

Big traffic data processing framework for intelligent monitoring and recording systems

Xia, Y., Chen, J., Lu, X., et al. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.140 · Cited by 34

A neural network architecture for incremental learning

Shiotani, S., Fukuda, T., & Shibata, T. (1995) · Neurocomputing · doi:10.1016/0925-2312(94)00061-v · Cited by 34

Serine Hydrolase KIAA1363: Toxicological and Structural Features with Emphasis on Organophosphate Interactions

Nomura, D. K., Durkin, K. A., Chiang, K. P., Quistad, G. B., Cravatt, B. F., & Casida, J. E. (2006) · Chemical Research in Toxicology · doi:10.1021/tx060117m · Cited by 34

Noise in neural networks: thresholds, hysteresis, and neuromodulation of signal-to-noise.

Keeler, J. D., Pichler, E. E., & Ross, J. (1989) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.86.5.1712 · Cited by 34

On the equivalence and properties of noisy neural and probabilistic RAM nets

Gorse, D., & Taylor, J. G. (1988) · Physics Letters A · doi:10.1016/0375-9601(88)90782-7 · Cited by 34

A Neural Network Trained to Identify the Presence of Myocardial Infarction Bases Some Decisions on Clinical Associations That Differ from Accepted Clinical Teaching

Baxt, W. G. (1994) · Medical Decision Making · doi:10.1177/0272989x9401400303 · Cited by 34

Are connectionist models cognitive?

Shanon, B. (1992) · Philosophical Psychology · doi:10.1080/09515089208573059 · Cited by 34

Artificial intelligence and molecular biology

(1994) · Choice Reviews Online · doi:10.5860/choice.31-3826 · Cited by 34

A Spiking Neuron Model of Word Associations for the Remote Associates Test

Kajić, I., Gosmann, J., Stewart, T. C., Wennekers, T., & Eliasmith, C. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00099 · Cited by 29

Comparative Study of Fuzzy Control, Neural Network Control and Neuro-Fuzzy Control

Godjevac, J. (1995) · International series in intelligent technologies · doi:10.1007/978-1-4615-2357-4_12 · Cited by 31

Comparison of WAVENET and ANN for predicting the porosity obtained from well log data

Saljooghi, B. S., & Hezarkhani, A. (2014) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2014.08.025 · Cited by 33

Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size

Backhaus, A., & Udo Seiffert, A. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.09.048 · Cited by 34

Incorporating Web Analysis Into Neural Networks: An Example in Hopfield Net Searching

Chau, M., & Chen, H. (2007) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.893277 · Cited by 32

Using Connectionist Models to Evaluate Examinees’ Response Patterns to Achievement Tests

Gierl, M. J., Cui, Y. D., & Hunka, S. (2008) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1209615480 · Cited by 33

Using Neural Networks to Predict Dye Concentrations in Multiple-Dye Mixtures

Jasper, W. J., Kovacs, E. T., & Berkstresser, G. A. (1993) · Textile Research Journal · doi:10.1177/004051759306300907 · Cited by 33

Accurate scatter compensation using neural networks in radionuclide imaging

Ogawa, K., & Nishizaki, N. (1993) · IEEE Transactions on Nuclear Science · doi:10.1109/23.256705 · Cited by 33

Rapid and quantitative analysis of recombinant protein expression using pyrolysis mass spectrometry and artificial neural networks: application to mammalian cytochrome b5 in Escherichia coli

Goodacre, R., Karim, A., Kaderbhai, M. A., et al. (1994) · Journal of Biotechnology · doi:10.1016/0168-1656(94)90088-4 · Cited by 33

A Boltzmann machine for the organization of intelligent machines

Moed, M. C., & Saridis, G. N. (1990) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.59972 · Cited by 33

Transient stability assessment by pattern recognition in the frequency domain

Ostojic, D. R., & Heydt, G. T. (1991) · IEEE Transactions on Power Systems · doi:10.1109/59.131067 · Cited by 33

Prediction of flank wear of different coated drills for JIS SUS 304 stainless steel using neural network

Tsao, C. (2002) · Journal of Materials Processing Technology · doi:10.1016/s0924-0136(01)01257-2 · Cited by 33

Ultrasonic robot eyes using neural networks

Watanabe, S., & Yoneyama, M. (1990) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/58.55303 · Cited by 33

Analyzing vision at the complexity level: Misplaced complexity?

Krueger, L. E., & Tsav, C.-Y. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00079644 · Cited by 32

Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

Tarafder, P., & Choi, W. (2023) · Sensors · doi:10.3390/s23052772 · Cited by 32

Agent-Based Models of Science

Payette, N. (2011) · Understanding complex systems · doi:10.1007/978-3-642-23068-4_4 · Cited by 32

Prognostics of aluminum electrolytic capacitors using artificial neural network approach

Khera, N., & Khan, S. A. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.11.002 · Cited by 32

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Puskarczyk, E. (2020) · Energies · doi:10.3390/en13071548 · Cited by 32

Bayesian and Neural Network Approaches to Estimate Deep Temperature Distribution for Assessing a Supercritical Geothermal System: Evaluation Using a Numerical Model

Ishitsuka, K., Kobayashi, Y., Watanabe, N., et al. (2021) · Natural Resources Research · doi:10.1007/s11053-021-09874-w · Cited by 32

Computer Simulations

Humphreys, P. W. (1990) · PSA Proceedings of the Biennial Meeting of the Philosophy of Science Association · doi:10.1086/psaprocbienmeetp.1990.2.193093 · Cited by 32

Accuracy enhancement of multi-axis CNC machines through on-line neurocompensation

Ouafi, A. E., Guillot, M., & Bedrouni, A. (2000) · Journal of Intelligent Manufacturing · doi:10.1023/a:1026500422130 · Cited by 32

Energy-based probabilistic evaluation of soil liquefaction

Chen, Y.-R., Hsieh, S.-C., Chen, J. W., et al. (2004) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2004.07.002 · Cited by 32

Incipient multiple fault diagnosis in real time with application to large-scale systems

Chung, H. Y., Bien, Z. N., Park, J.-H., et al. (1994) · IEEE Transactions on Nuclear Science · doi:10.1109/23.322777 · Cited by 32

Autobiographical Remembering: Creating Personal Culture

Barclay, C. R., & Smith, T. S. (1992) · Springer eBooks · doi:10.1007/978-94-015-7967-4_5 · Cited by 32

Automated conversion of engineering drawings to CAD form

Filipski, A. J., & Flandrena, R. (1992) · Proceedings of the IEEE · doi:10.1109/5.156479 · Cited by 31

Paralleled hardware annealing for optimal solutions on electronic neural networks

Lee, B. W., & Sheu, B. J. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.238314 · Cited by 32

Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues

Celeghin, A., Borriero, A., Orsenigo, D., Diano, M., Guerrero, C. A. M., Perotti, A., Petri, G., & Tamietto, M. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1153572 · Cited by 50

Artificial odor-recognition system using neural network for estimating sensory quantities of blended fragrance

Hanaki, S.-I., Nakamoto, T., & Moriizumi, T. (1996) · Sensors and Actuators A Physical · doi:10.1016/s0924-4247(97)80096-9 · Cited by 31

The allure of connectionism reexamined

McLaughlin, B. P., & Warfield, T. A. (1994) · Synthese · doi:10.1007/bf01063895 · Cited by 23

Connectionist approaches to development

(1996) · Infant Behavior and Development · doi:10.1016/s0163-6383(96)90232-5

Connectionism in a broad perspective : selected papers from the Swedish Conference on Connectionism, 1992

Niklasson, L., & Bodén, M. (1994) · Ellis Horwood eBooks · Cited by 2

What Should a Connectionist Philosophy of Science Look Like

McCauley, R. N., & Bechtel, W. (2002) · Cited by 34

Connectionism and Systematicity

Matthews, R. J. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00164 · Cited by 2