Abstract
We present a method for computationally efficient cortical brain simulation by constructing a 2D cortical flat map space on a regular grid. Neuroscience data can be mapped into this space to provide experimental information and constraints for the simulation. Neuron locations can be determined probabilistically by treating neuron densities as empirical probability distributions that can be sampled from. Therefore, this approach can be used for specifying parameters for small-scale to large-scale brain simulations (that could simulate the true number of neurons in the brain). The spatial warping of the cortical surface, when going between the flattened 2D space back into 3D, is accounted for by an estimated scale factor. This can be used to scale properties such as diffusion rates of neural activity across the flat map. We demonstrate the approach using neuroimaging data of the common marmoset, a New World primate.
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Notes
- 1.
Some versions of the model add a diffusion term to w to give:
$$\frac{\partial w}{\partial t} = D_w \nabla ^2 w + (v - \gamma w - \beta )$$.
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Acknowledgements
The authors wish to thank Drs. Jun Igarashi and Hiromich Tsukada for their valuable discussions on the topic of brain simulation. This research was supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development, AMED. Grant number: JP15dm0207001 to A.W. and R.G., JP19dm0207088 to K.N.
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Woodward, A., Gong, R., Nakae, K., Delmas, P. (2023). A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain Simulation. In: Blanc-Talon, J., Delmas, P., Philips, W., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2023. Lecture Notes in Computer Science, vol 14124. Springer, Cham. https://doi.org/10.1007/978-3-031-45382-3_27
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