Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017)

"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."

Model Type: Realistic Network; Synapse

Region(s) or Organism(s): Hippocampus; Basal ganglia; Cerebellum; Striatum

Cell Type(s): Cerebellum Purkinje GABA cell

Transmitters: Gaba; Glutamate

Model Concept(s): Learning; Synaptic Plasticity

Simulation Environment: MATLAB; MATLAB (web link to model)

Implementer(s): Scheler, Gabriele [gscheler at gmail.com]


Scheler G. (2017). Logarithmic distributions prove that intrinsic learning is Hebbian. F1000Research. 6 [PubMed]

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.