ArticleFrontiers in computational neuroscience2022
Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning.
Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.Research square · 2026Article
- Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.Science advances · 2026Article
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.bioRxiv : the preprint server for biology · 2026Article
- DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation.bioRxiv : the preprint server for biology · 2026Article
- Evolving spiking neural networks: the role of neuron models and encoding schemes in neuromorphic learning.Frontiers in neuroscience · 2026Article
- Spike-based Q-learning in a non-von Neumann architecture.Frontiers in neuroscience · 2026Article
- Spark: modular spiking neural networks.Frontiers in artificial intelligence · 2026Article
- Differentiable simulation expands frontiers for biophysical neural models.Nature methods · 2025Article
- Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Reward-optimizing learning using stochastic release plasticity.Frontiers in neural circuits · 2025Article
- Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.PLoS computational biology · 2024Article
- Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Review
- Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics.Cell reports · 2023Article
- Incorporating structural plasticity into self-organization recurrent networks for sequence learning.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning
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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 Socratic graph.