"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."
Reference:
1 .
Scheler G (2017) Logarithmic distributions prove that intrinsic learning is Hebbian. F1000Res 6:1222 [PubMed]
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