ArticleProceedings of the National Academy of Sciences of the United States of America2019
Simple framework for constructing functional spiking recurrent neural networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network.iScience · 2026Article
- Disinhibitory signaling enables flexible coding of top-down information in cortical networks.PLoS biology · 2026Article
- Data-Constrained Recurrent Network Neural Model Uncovers the Circuit Mechanism of Olfactory OFF Responses.bioRxiv : the preprint server for biology · 2026Article
- Balancing Stability and Flow in Hippocampal Networks via Inductive Bias and Learned Symmetry Breaking.bioRxiv : the preprint server for biology · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Neuromodulators Generate Multiple Context-Relevant Behaviors in Recurrent Neural Networks.Neural computation · 2026Article
- Spark: modular spiking neural networks.Frontiers in artificial intelligence · 2026Article
- Article
- Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- Unraveling the neural code: analysis of large-scale two-photon microscopy data.Microscopy (Oxford, England) · 2025Review
- 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
- The backpropagation algorithm implemented on spiking neuromorphic hardware.Nature communications · 2024Article
- Probing latent brain dynamics in Alzheimer's disease via recurrent neural network.Cognitive neurodynamics · 2024Article
- Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity.Science advances · 2024Article
- Connectome-based reservoir computing with the conn2res toolbox.Nature communications · 2024Article
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights.Frontiers in systems neuroscience · 2024Article
- Recurrent neural network dynamical systems for biological vision.Advances in neural information processing systems · 2024Article
- Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies.bioRxiv : the preprint server for biology · 2023Article
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3 authors.
Funding
Abstract
Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast, continuous models of rate-coding neurons can be trained to perform complex tasks. Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks. Our framework involves training a continuous-variable rate RNN with important biophysical constraints and transferring the learned dynamics and constraints to a spiking RNN in a one-to-one manner. The proposed framework introduces only 1 additional parameter to establish the equivalence between rate and spiking RNN models. We also study other model parameters related to the rate and spiking networks to optimize the one-to-one mapping. By establishing a close relationship between rate and spiking models, we demonstrate that spiking RNNs could be constructed to achieve similar performance as their counterpart continuous rate networks.
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