ArticleF1000Research2017
Logarithmic distributions prove that intrinsic learning is Hebbian.
Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Learning and criticality in a self-organizing model of connectome growth.Scientific reports · 2025Article
- Volume electron microscopy reveals unique laminar synaptic characteristics in the human entorhinal cortex.eLife · 2025Article
- Principles of visual cortex excitatory microcircuit organization.Innovation (Cambridge (Mass.)) · 2025Article
- Weber-Fechner law in temporal difference learning derived from control as inference.Frontiers in robotics and AI · 2025Article
- Weber's Law as the emergent phenomenon of choices based on global inhibition.Frontiers in neuroscience · 2025Article
- Hierarchical organization of human physical activity.Scientific reports · 2024Article
- Linking spontaneous and stimulated spine dynamics.Communications biology · 2023Article
- Skewed distribution of spines is independent of presynaptic transmitter release and synaptic plasticity, and emerges early during adult neurogenesis.Open biology · 2023Article
- Long-Term Changes in Axon Calibers after Injury: Observations on the Mouse Corticospinal Tract.International journal of molecular sciences · 2022Article
- Dynamical Mechanism Underlying Scale-Free Network Reorganization in Low Acetylcholine States Corresponding to Slow Wave Sleep.Frontiers in network physiology · 2021Article
- Activity Dependent and Independent Determinants of Synaptic Size Diversity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Neuromodulation influences synchronization and intrinsic read-out.F1000Research · 2018Article
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1 author.
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Abstract
In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability.
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