ArticleScientific reports2017
Symmetry of learning rate in synaptic plasticity modulates formation of flexible and stable memories.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex.PLoS computational biology · 2023Article
- Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity.Scientific reports · 2021Article
- Inhibition of RhoA/Rho kinase signaling pathway by fasudil protects against kainic acid-induced neurite injury.Brain and behavior · 2021Article
- Visual number sense in untrained deep neural networks.Science advances · 2021Article
- Spontaneous Retinal Waves Can Generate Long-Range Horizontal Connectivity in Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Article
- Learning spatiotemporal signals using a recurrent spiking network that discretizes time.PLoS computational biology · 2020Article
- Biological learning curves outperform existing ones in artificial intelligence algorithms.Scientific reports · 2019Article
- Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks.Scientific reports · 2018Article
- Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.Scientific reports · 2018Article
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3 authors.
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Abstract
Spike-timing-dependent plasticity (STDP) is considered critical to learning and memory functions in the human brain. Across various types of synapse, STDP is observed as different profiles of Hebbian and anti-Hebbian learning rules. However, the specific roles of diverse STDP profiles in memory formation still remain elusive. Here, we show that the symmetry of the learning rate profile in STDP is crucial to determining the character of stored memory. Using computer simulations, we found that an asymmetric learning rate generates flexible memory that is volatile and easily overwritten by newly appended information. Moreover, a symmetric learning rate generates stable memory that can coexist with newly appended information. In addition, by combining these two conditions, we could realize a hybrid memory type that operates in a way intermediate between stable and flexible memory. Our results demonstrate that various attributes of memory functions may originate from differences in the synaptic stability.
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