ArticleScientific reports2018
Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Microcircuit Synchronization and Heavy-Tailed Synaptic Weight Distribution Augment preBötzinger Complex Bursting Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- A convolutional neural network for estimating synaptic connectivity from spike trains.Scientific reports · 2021Article
- Activity Dependent and Independent Determinants of Synaptic Size Diversity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
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Authors and funding
5 authors.
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Abstract
Experimental evidence recently indicated that neural networks can learn in a different manner than was previously assumed, using adaptive nodes instead of adaptive links. Consequently, links to a node undergo the same adaptation, resulting in cooperative nonlinear dynamics with oscillating effective link weights. Here we show that the biological reality of stationary log-normal distribution of effective link weights in neural networks is a result of such adaptive nodes, although each effective link weight varies significantly in time. The underlying mechanism is a stochastic restoring force emerging from a spontaneous temporal ordering of spike pairs, generated by strong effective link preceding by a weak one. In addition, for feedforward adaptive node networks the number of dynamical attractors can scale exponentially with the number of links. These results are expected to advance deep learning capabilities and to open horizons to an interplay between adaptive node rules and the distribution of network link weights.
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Registered trials
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