ArticlePloS one2019
Weight statistics controls dynamics in recurrent neural 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 15 papers.
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15 citing papers in PubMed.
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- Analysis of argument structure constructions in a deep recurrent language model.Frontiers in computational neuroscience · 2025Article
- Analysis of argument structure constructions in the large language model BERT.Frontiers in artificial intelligence · 2025Article
- Coincidence detection and integration behavior in spiking neural networks.Cognitive neurodynamics · 2024Article
- The connectivity degree controls the difficulty in reservoir design of random boolean networks.Frontiers in computational neuroscience · 2024Article
- Extracting continuous sleep depth from EEG data without machine learning.Neurobiology of sleep and circadian rhythms · 2023Article
- Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts.Scientific reports · 2023Article
- Excitatory/inhibitory balance emerges as a key factor for RBN performance, overriding attractor dynamics.Frontiers in computational neuroscience · 2023Article
- Classification at the accuracy limit: facing the problem of data ambiguity.Scientific reports · 2022Article
- Control of noise-induced coherent oscillations in three-neuron motifs.Cognitive neurodynamics · 2022Article
- Dynamics and Information Import in Recurrent Neural Networks.Frontiers in computational neuroscience · 2022Article
- Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway.Frontiers in neuroscience · 2022Article
- Analysis and visualization of sleep stages based on deep neural networks.Neurobiology of sleep and circadian rhythms · 2021Article
- Recurrence Resonance" in Three-Neuron Motifs.Frontiers in computational neuroscience · 2019Article
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6 authors.
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Abstract
Recurrent neural networks are complex non-linear systems, capable of ongoing activity in the absence of driving inputs. The dynamical properties of these systems, in particular their long-time attractor states, are determined on the microscopic level by the connection strengths wij between the individual neurons. However, little is known to which extent network dynamics is tunable on a more coarse-grained level by the statistical features of the weight matrix. In this work, we investigate the dynamics of recurrent networks of Boltzmann neurons. In particular we study the impact of three statistical parameters: density (the fraction of non-zero connections), balance (the ratio of excitatory to inhibitory connections), and symmetry (the fraction of neuron pairs with wij = wji). By computing a 'phase diagram' of network dynamics, we find that balance is the essential control parameter: Its gradual increase from negative to positive values drives the system from oscillatory behavior into a chaotic regime, and eventually into stationary fixed points. Only directly at the border of the chaotic regime do the neural networks display rich but regular dynamics, thus enabling actual information processing. These results suggest that the brain, too, is fine-tuned to the 'edge of chaos' by assuring a proper balance between excitatory and inhibitory neural connections.
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