Neural number analysis: why mammalian brains prefer natural logarithmic patterns

Neural number analysis: why mammalian brains prefer natural logarithmic patterns

summary: New research looks at how statistical distributions shape the neuron densities of mammalian brains.

The study analyzed seven species, and discovered that neuron densities follow a lognormal distribution – a key organizing principle. This distribution is characterized by its asymmetric curve and is important for understanding brain connectivity and brain-inspired technology design.

Since many of the brain’s features align with this distribution, it points to its potential computational benefits.

Key facts:

  1. Neuron densities in the brains of seven mammalian species studied, including humans, follow a consistent log-normal distribution pattern.
  2. The lognormal distribution arises as a result of multiplicative processes and affects network connectivity within the brain.
  3. Heterogeneity in brain connectivity, which is likely related to lognormal distribution, can enhance information transmission, learning and memory capabilities in neural circuits.

source: The Human Brain Project

Neuron numbers and their spatial arrangement play a critical role in shaping brain structure and function. However, despite the wealth of cytoarchitecture data available, the statistical distributions of neuronal densities remain largely uncharacterised.

The new HBP study, published in cerebral cortexenhances our understanding of the organization of mammalian brains.

The team based its investigation on nine publicly available datasets for seven species: mouse, monkey, macaque, galago, owl monkey, baboon and human. After analyzing the cortical regions of each, they found that the neuron densities within these regions followed a consistent pattern — a lognormal distribution.

These results are consistent with the observation that many brain properties follow a normal zonal distribution. Credit: Neuroscience News

This points to a fundamental organizing principle that underlies neuronal densities in the mammalian brain.

A lognormal distribution is a statistical distribution characterized by a skewed, bell-shaped curve. It arises, for example, when taking the exponent of a normally distributed variable. It differs from a normal distribution in several ways. More importantly, the normal distribution curve is symmetric, while the lognormal curve is asymmetric and has a heavy tail.

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These findings are relevant for accurate brain modeling.

“Not least because the distribution of neuronal densities influences network connectivity,” says Sascha van Alpada, Group Leader of Theoretical Neuroanatomy at Forschungszentrum Jülich and senior author of the paper.

“For example, if the density of synapses is constant, then regions with lower neuron density will receive more synapses per neuron,” she explains. These aspects are also relevant to the design of brain-inspired technology such as neuromodulators.

“Moreover, since cortical regions are often distinguished on the basis of cytoarchitecture, knowledge of the distribution of neuronal densities can be relevant to the statistical assessment of differences between regions and the locations of boundaries between regions,” Van Alpada adds.

These results are consistent with the observation that many brain properties follow a normal zonal distribution. “One of the reasons they are so common in nature is that they appear when the product of many independent variables is taken into account,” says Alexander van Meijn, co-first author of the study.

In other words, a lognormal distribution arises naturally as a result of multiplication operations, similar to how a normal distribution appears when many independent variables are added.

“Using a simple model, we were able to show how the doubling of neurons during development might lead to the observed neuron density distributions,” van Meijn explains.

According to the study, in principle, organizational structures at the level of the cerebral cortex may be by-products of development or development and do not serve any computational function; But the fact that the same organizational structures can be observed for many species and across most cortical regions suggests that the log-normal distribution serves a purpose.

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“We cannot be sure how the log-normal distribution of neuron densities affects brain function, but it is likely related to the high network heterogeneity, which could be computationally advantageous,” says Aitor Morales Gregorio, first author of the study, citing previous work. which suggest that heterogeneity in brain connectivity may promote efficient information transmission.

In addition, heterogeneous networks support robust learning and enhance the memory capacity of neural circuits.

About this research in Neuroscience News

author: Peter Zekert
source: The Human Brain Project
communication: Peter Zekert – The Human Brain Project
picture: Image credited to Neuroscience News

Original search: open access.
Ubiquity lognormal distribution of neuron density in the mammalian cerebral cortexWritten by Sasha Van Albada et al. cerebral cortex

a summary

Ubiquity lognormal distribution of neuron density in the mammalian cerebral cortex

Neuron numbers and their spatial diversity are essential organizational features of the brain. Despite the large amount of cytoarchitecture data available in the literature, the statistical distributions of neuronal densities within and across brain regions remain largely ill-defined.

Here we show that neuronal densities are consistent with a log-normal distribution across cortical regions in many mammalian species, and we find that this is also true for cortical regions.

A simple model of noisy cell division, in combination with distributed diffusion times, can explain the coexistence of log-normal distributions within and across cortical regions.

Our findings reveal a new organizing principle of cortical cellular architecture: the ubiquitous lognormal distribution of neuronal densities, which adds to a long list of lognormal variables in the brain.

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