ReviewFrontiers in computational neuroscience2023
Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.
Review in Frontiers in computational neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The pathogenic theory of homeostasis threshold deviation (HTD) in wide-range oscillatory physiological parameters: novel perspectives for hypertension and metabolic disease treatment.Life metabolism · 2025Review
- Biologically inspired neural network layer with homeostatic regulation and adaptive repair mechanisms.Scientific reports · 2025Article
- A general framework for interpretable neural learning based on local information-theoretic goal functions.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Emergent Self-Adaptation in an Integrated Photonic Neural Network for Backpropagation-Free Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Computing with oscillators from theoretical underpinnings to applications and demonstrators.Npj unconventional computing · 2024Review
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Authors and funding
2 authors.
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Abstract
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.
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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.