ArticleCell2019
Continual Learning in a Multi-Layer Network of an Electric Fish.
Article in Cell, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 32 citations in OpenAlex.
- Article
- Critical period plasticity enables credit assignment.bioRxiv : the preprint server for biology · 2026Article
- Desegregation of neuronal predictive processing.Nature communications · 2026Article
- Article
- An internal model for canceling self-generated sensory input in freely behaving electric fish.Neuron · 2023Article
- A mechanism for differential control of axonal and dendritic spiking underlying learning in a cerebellum-like circuit.Current biology : CB · 2023Article
- Article
- Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks.Science advances · 2021Article
- Population coding in the cerebellum: a machine learning perspective.Journal of neurophysiology · 2020Review
- Backpropagation and the brain.Nature reviews. Neuroscience · 2020Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems.
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What OpenQuestion holds
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.