ArticlePloS one2019
Training dynamically balanced excitatory-inhibitory networks.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network.iScience · 2026Article
- Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning.Science advances · 2025Article
- Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks.Nature communications · 2025Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- CONSTRUCTING BIOLOGICALLY CONSTRAINED RNNS VIA DALE'S BACKPROP AND TOPOLOGICALLY-INFORMED PRUNING.bioRxiv : the preprint server for biology · 2025Article
- Effect in the spectra of eigenvalues and dynamics of RNNs trained with excitatory-inhibitory constraint.Cognitive neurodynamics · 2024Article
- Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights.Frontiers in systems neuroscience · 2024Article
- Geometry of population activity in spiking networks with low-rank structure.PLoS computational biology · 2023Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Distributing task-related neural activity across a cortical network through task-independent connections.Nature communications · 2023Article
- Article
- Different eigenvalue distributions encode the same temporal tasks in recurrent neural networks.Cognitive neurodynamics · 2023Article
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics.Frontiers in computational neuroscience · 2023Article
- Input correlations impede suppression of chaos and learning in balanced firing-rate networks.PLoS computational biology · 2022Article
- Error-based or target-based? A unified framework for learning in recurrent spiking networks.PLoS computational biology · 2022Article
- Oscillatory beta/alpha band modulations: A potential biomarker of functional language and motor recovery in chronic stroke?Frontiers in human neuroscience · 2022Review
- Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making.Frontiers in neuroscience · 2022Article
- MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks.Frontiers in neuroscience · 2022Article
- Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.Neural computation · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networks composed of separate excitatory and inhibitory neurons obeying Dale's law presents a number of challenges. We show how a target-based approach, when combined with a fast online constrained optimization technique, is capable of building functional models of rate and spiking recurrent neural networks in which excitation and inhibition are balanced. Balanced networks can be trained to produce complicated temporal patterns and to solve input-output tasks while retaining biologically desirable features such as Dale's law and response variability.
Indexed as
Identifiers
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.