ReviewFrontiers in computational neuroscience2017
Computational Foundations of Natural Intelligence.
Review in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset.Diagnostics (Basel, Switzerland) · 2024Article
- Eight challenges in developing theory of intelligence.Frontiers in computational neuroscience · 2024Article
- Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception.Brain : a journal of neurology · 2023Review
- Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks.Journal of computational neuroscience · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Artificial Intelligence for Cancer Detection-A Bibliometric Analysis and Avenues for Future Research.Current oncology (Toronto, Ont.) · 2023Review
- Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.Frontiers in cardiovascular medicine · 2023Article
- Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Encoder-Decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation.IEEE transactions on neural networks and learning systems · 2021Article
- CNN-Based Volume Flow Rate Prediction of Oil-Gas-Water Three-Phase Intermittent Flow from Multiple Sensors.Sensors (Basel, Switzerland) · 2021Article
- Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation.Brain sciences · 2020Article
- Will We Ever Have Conscious Machines?Frontiers in computational neuroscience · 2020Article
- Precise Modeling of the Protective Effects of Quercetin against Mycotoxin via System Identification with Neural Networks.International journal of molecular sciences · 2019Article
- Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug Development.Methods in molecular biology (Clifton, N.J.) · 2019Review
- Transferring and generalizing deep-learning-based neural encoding models across subjects.NeuroImage · 2018Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.