ArticlePLoS computational biology2020
Learning to represent signals spike by spike.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 11 citations in OpenAlex.
- Spiking neurons as predictive controllers of linear systems.PLoS computational biology · 2026Article
- Desegregation of neuronal predictive processing.Nature communications · 2026Article
- Locality and weight sharing shape Hebbian principal-component learning in biologically constrained visual models.Frontiers in computational neuroscience · 2026Article
- Constrained Hebbian learning supports efficient representational allocation under structural constraints.Frontiers in computational neuroscience · 2026Article
- Predictive Coding Light.Nature communications · 2025Article
- Representational drift without synaptic plasticity.bioRxiv : the preprint server for biology · 2025Article
- Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates.PLoS computational biology · 2025Article
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
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- Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.bioRxiv : the preprint server for biology · 2025Article
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- Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex.bioRxiv : the preprint server for biology · 2024Article
- The impact of spike timing precision and spike emission reliability on decoding accuracy.Scientific reports · 2024Article
- Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections.Nature communications · 2023Article
- Establishing brain states in neuroimaging data.PLoS computational biology · 2023Article
- Omission responses in local field potentials in rat auditory cortex.BMC biology · 2023Article
- Biophysical parameters control signal transfer in spiking network.Frontiers in computational neuroscience · 2023Article
- Computational methods to study information processing in neural circuits.Computational and structural biotechnology journal · 2023Review
- Predictive coding is a consequence of energy efficiency in recurrent neural networks.Patterns (New York, N.Y.) · 2022Article
- Volitional Generation of Reproducible, Efficient Temporal Patterns.Brain sciences · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Networks based on coordinated spike coding can encode information with high efficiency in the spike trains of individual neurons. These networks exhibit single-neuron variability and tuning curves as typically observed in cortex, but paradoxically coincide with a precise, non-redundant spike-based population code. However, it has remained unclear whether the specific synaptic connectivities required in these networks can be learnt with local learning rules. Here, we show how to learn the required architecture. Using coding efficiency as an objective, we derive spike-timing-dependent learning rules for a recurrent neural network, and we provide exact solutions for the networks' convergence to an optimal state. As a result, we deduce an entire network from its input distribution and a firing cost. After learning, basic biophysical quantities such as voltages, firing thresholds, excitation, inhibition, or spikes acquire precise functional interpretations.
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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.