3D mechanistic ABM for neuronal growth. Panel a shows the agent-based discretization and early stage of a neuron (pyramid cell) simulation. The center is the spherical soma (cell body). The dendrites are discretized by small cylindrical agents. Typically, tipping drives growth (Shree et al. 2022); therefore, we differentiate between general agents and tipping agents. The agents define a tree structure with different segments and branches. b Pyramidal cell at the end of simulation. c Experimental pyramidal cell in the mouse hippocampus observed by Benavides-Piccione et al. (2019) (NMO_147071). Credit: Journal of Mathematical Biology (2024). DOI: 10.1007/s00285-024-02144-2
A new computer simulation of how our brains develop and grow neurons has been built by scientists at the University of Surrey. In addition to improving our understanding of how the brain works, researchers hope the models will contribute to research into neurodegenerative diseases and, one day, research into stem cells that help regenerate brain tissue.
The research was published in the Journal of Mathematical Biology.
The research team used a technique called approximate Bayesian computing (ABC), which helps refine the model by comparing the simulation with actual neuron growth. This process ensures that the artificial brain accurately reflects the way neurons develop and make connections in real life.
The simulation was tested using neurons from the hippocampus, a critical region of the brain involved in memory retention. The team found that their system successfully mimicked the growth patterns of real hippocampal neurons, demonstrating the potential of this technology to simulate brain development in great detail.
Dr Roman Bauer, from the School of Computer Science and Electronic Engineering at the University of Surrey, said: “How our brains work remains one of the greatest scientific mysteries. With this simulation and rapid advances in artificial intelligence, we are getting closer to understanding how neurons develop and communicate. We hope that one day this work can lead to better treatments for devastating diseases like Alzheimer’s or Parkinson’s, which will change the lives of millions of people.
The accuracy of the model is closely linked to the quality of the data used to calibrate it. If the actual neuron data is limited or incomplete, the accuracy of the simulation may decrease. Although the current model showed impressive results in reproducing the growth of specific neurons, such as hippocampal pyramidal cells, additional adjustments may be needed to accurately simulate other neuron types or regions of the hippocampus. brain.
The computer simulation is built using BioDynaMo software, co-developed by Dr. Bauer. The software helps scientists easily create, run and visualize multidimensional agent-based simulations, whether biological, sociological, ecological or financial.
More information:
Tobias Duswald et al, Calibration of agent-based stochastic neuronal growth models with approximate Bayesian computation, Journal of Mathematical Biology (2024). DOI: 10.1007/s00285-024-02144-2
Provided by the University of Surrey
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