ArticleProceedings of the National Academy of Sciences of the United States of America2021
Cell-type-specific neuromodulation guides synaptic credit assignment in a spiking neural network.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 31 citations in OpenAlex.
- Diffusion of neuromodulators for temporal credit assignment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A cortical output channel for perceptual categorization.Science advances · 2026Article
- Neural rhythms as priors of speech computations.Patterns (New York, N.Y.) · 2026Article
- Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective.Patterns (New York, N.Y.) · 2025Review
- The neural basis for uncertainty processing in hierarchical decision making.Nature communications · 2025Article
- Learning neural dynamics through instructive signals.bioRxiv : the preprint server for biology · 2025Article
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- Direct excitatory loop and theta resonance in the anterior cingulate-mediodorsal thalamic circuit.Communications biology · 2025Article
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- Dynamic changes in the hippocampal neuronal circuits activity following acute stress revealed by miniature fluorescence microscopy imaging.Molecular brain · 2024Article
- Structured flexibility in recurrent neural networks via neuromodulation.Advances in neural information processing systems · 2024Article
- Adaptive robustness through incoherent signaling mechanisms in a regenerative brain.Cell reports · 2024Article
- Structured flexibility in recurrent neural networks via neuromodulation.bioRxiv : the preprint server for biology · 2024Article
- Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks.bioRxiv : the preprint server for biology · 2024Article
- Information Transmission in G Protein-Coupled Receptors.International journal of molecular sciences · 2024Review
- The reciprocity of skeletal muscle and bone: an evolving view from mechanical coupling, secretory crosstalk to stem cell exchange.Frontiers in physiology · 2024Review
- A role for cortical interneurons as adversarial discriminators.PLoS computational biology · 2023Article
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks.Frontiers in neuroscience · 2023Article
- Small, correlated changes in synaptic connectivity may facilitate rapid motor learning.Nature communications · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brains learn tasks via experience-driven differential adjustment of their myriad individual synaptic connections, but the mechanisms that target appropriate adjustment to particular connections remain deeply enigmatic. While Hebbian synaptic plasticity, synaptic eligibility traces, and top-down feedback signals surely contribute to solving this synaptic credit-assignment problem, alone, they appear to be insufficient. Inspired by new genetic perspectives on neuronal signaling architectures, here, we present a normative theory for synaptic learning, where we predict that neurons communicate their contribution to the learning outcome to nearby neurons via cell-type-specific local neuromodulation. Computational tests suggest that neuron-type diversity and neuron-type-specific local neuromodulation may be critical pieces of the biological credit-assignment puzzle. They also suggest algorithms for improved artificial neural network learning efficiency.
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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.