ArticleeLife2021
Evolving interpretable plasticity for spiking networks.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
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Who cites it
9 citing papers in PubMed, 32 citations in OpenAlex.
- Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.PLoS computational biology · 2025Article
- Neuroevolution insights into biological neural computation.Science (New York, N.Y.) · 2025Article
- Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems.Nature communications · 2024Article
- Auto-Spikformer: Spikformer architecture search.Frontiers in neuroscience · 2024Article
- Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware.Frontiers in 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
- Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks.Frontiers in neuroscience · 2023Article
- Meta-learning synaptic plasticity and memory addressing for continual familiarity detection.Neuron · 2022Article
- Analyzing time-to-first-spike coding schemes: A theoretical approach.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic coupling strength between neurons are essential for this capability, setting us apart from simpler, hard-wired organisms. How these changes can be mathematically described at the phenomenological level, as so-called 'plasticity rules', is essential both for understanding biological information processing and for developing cognitively performant artificial systems. We suggest an automated approach for discovering biophysically plausible plasticity rules based on the definition of task families, associated performance measures and biophysical constraints. By evolving compact symbolic expressions, we ensure the discovered plasticity rules are amenable to intuitive understanding, fundamental for successful communication and human-guided generalization. We successfully apply our approach to typical learning scenarios and discover previously unknown mechanisms for learning efficiently from rewards, recover efficient gradient-descent methods for learning from target signals, and uncover various functionally equivalent STDP-like rules with tuned homeostatic mechanisms.
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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.