ArticleNature communications2020
Control of criticality and computation in spiking neuromorphic networks with plasticity.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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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.
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Who cites it
42 citing papers in PubMed.
- Effects of spatial constraints of inhibitory connectivity on the dynamical development of criticality in spiking networks.PLoS computational biology · 2026Article
- Critical neuronal avalanches arise from excitation-inhibition balanced spontaneous activity.iScience · 2026Article
- Arousal-driven critical roaming reproduces human functional connectivity dynamics.PLoS biology · 2026Article
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application.Nano-micro letters · 2026Review
- The Electromechanical Connectome: Integrating Voltage, Mechanical Nano-Forces, and Subcellular Fluid Phase Dynamics in Human Neural Computation.International journal of molecular sciences · 2026Review
- Synergy mediates long-range correlations in the visual cortex near criticality.Frontiers in computational neuroscience · 2026Article
- Synergy mediates Long-Range Correlations in the Visual Cortex Near Criticality.bioRxiv : the preprint server for biology · 2025Article
- Adaptive proximity to criticality underlies amplification of ultra-slow fluctuations during free recall.PLoS computational biology · 2025Article
- Detecting Signatures of Criticality Using Divergence Rate.Entropy (Basel, Switzerland) · 2025Article
- Article
- Collective dynamics and long-range order in thermal neuristor networks.Nature communications · 2024Article
- Signatures of hierarchical temporal processing in the mouse visual system.PLoS computational biology · 2024Article
- Sleep restores an optimal computational regime in cortical networks.Nature neuroscience · 2024Article
- The connectivity degree controls the difficulty in reservoir design of random boolean networks.Frontiers in computational neuroscience · 2024Article
- The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not in the activity of individual neurons.bioRxiv : the preprint server for biology · 2023Article
- Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks.Nature communications · 2023Article
- Modular architecture facilitates noise-driven control of synchrony in neuronal networks.Science advances · 2023Article
- Neuronal avalanches: Sandpiles of self-organized criticality or critical dynamics of brain waves?Frontiers of physics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. To that end, we developed a plastic spiking network on a neuromorphic chip. We show that the distance to criticality can be easily adapted by changing the input strength, and then demonstrate a clear relation between criticality, task-performance and information-theoretic fingerprint. Whereas the information-theoretic measures all show that network capacity is maximal at criticality, only the complex tasks profit from criticality, whereas simple tasks suffer. Thereby, we challenge the general assumption that criticality would be beneficial for any task, and provide instead an understanding of how the collective network state should be tuned to task requirement.
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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.