ArticleNature communications2017
Supervised learning in spiking neural networks with FORCE training.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
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Who cites it
85 citing papers in PubMed.
- Deep-learning-assisted simulation of a cortical circuit: integrating anatomy, physiology and function.bioRxiv : the preprint server for biology · 2026Article
- Online supervised learning of temporal patterns in biological neural networks under feedback control.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Article
- Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning.Science advances · 2025Article
- Neuromorphic computing paradigms enhance robustness through spiking neural networks.Nature communications · 2025Article
- A genetic algorithm for self-supervised models of oscillatory neurodynamics.bioRxiv : the preprint server for biology · 2025Article
- Dynamical mean-field theory for a highly heterogeneous neural population with graded persistent activity of the entorhinal cortex.PLoS computational biology · 2025Article
- Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks.Nature communications · 2025Article
- Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks.Nature communications · 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
- Article
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- Temporal resolution of spike coding in feedforward networks with signal convergence and divergence.PLoS computational biology · 2025Article
- Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- CONSTRUCTING BIOLOGICALLY CONSTRAINED RNNS VIA DALE'S BACKPROP AND TOPOLOGICALLY-INFORMED PRUNING.bioRxiv : the preprint server for biology · 2025Article
- Hippocampal phase precession may be generated by chimera dynamics.Frontiers in neural circuits · 2025Article
- Neural heterogeneity as a unifying mechanism for efficient learning in spiking neural networks.Frontiers in computational neuroscience · 2025Article
- Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modelingFrontiers in neuroscience · 2025Article
- Discovering plasticity rules that organize and maintain neural circuits.bioRxiv : the preprint server for biology · 2024Article
25 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.
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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.