Abstract
The morphology of pyramidal cells (PCs) varies significantly among species and brain layers. Therefore, it is particularly challenging to analyze which species or layers they belong to based on morphological features. Existing deep learning-based methods analyze species-related or layer-related morphological characteristics of PCs. However, these methods are realized in a task-agnostic manner without considering task-specific features. This paper proposes a task-specific morphological representation learning framework for morphology analysis of PCs to enforce task-specific feature extraction through dual-task learning, enabling performance gains for each task. Specifically, we first utilize species-wise and layer-wise feature extraction branches to obtain species-related and layer-related features. Applying the principle of mutual information minimization, we then explicitly force each branch to learn task-specific features, which are further enhanced via an adaptive representation enhancement module. In this way, the performance of both tasks can be greatly improved simultaneously. Experimental results demonstrate that the proposed method can effectively extract the species-specific and layer-specific representations when identifying rat and mouse PCs in multiple brain layers. Our method reaches the accuracies of 87.44% and 72.46% on species and layer analysis tasks, significantly outperforming a single task by 2.22% and 3.86%, respectively.
C. Sun and Q. Guo—Equal contributions.
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Notes
- 1.
Note that task-related features contain task-specific and common features.
References
Ascoli, G.A., Donohue, D.E., Halavi, M.: Neuromorpho. org: a central resource for neuronal morphologies. J. Neurosci. 27(35), 9247–9251 (2007)
Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 15509–15519 (2019)
Batabyal, T., Condron, B., Acton, S.T.: Neuropath2path: classification and elastic morphing between neuronal arbors using path-wise similarity. Neuroinformatics 18(3), 479–508 (2020)
Bekkers, J.M.: Pyramidal neurons. Curr. Biol. 21(24), R975 (2011)
Belghazi, M.I., et al.: Mutual information neural estimation. In: Proceedings of the International Conference on Machine Learning, pp. 531–540 (2018)
Chen, X., et al.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 2180–2188 (2016)
Cheng, P., et al.: Club: a contrastive log-ratio upper bound of mutual information. In: Proceedings of the International Conference on Machine Learning, pp. 1779–1788 (2020)
Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, pp. 248–255 (2009)
Elston, G.N.: Cortex, cognition and the cell: new insights into the pyramidal neuron and prefrontal function. Cereb. Cortex 13(11), 1124–1138 (2003)
Gao, W.J., Zheng, Z.H.: Target-specific differences in somatodendritic morphology of layer v pyramidal neurons in rat motor cortex. J. Comp. Neurol. 476(2), 174–185 (2004)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, pp. 770–778 (2016)
Hou, X., Li, Y., Wang, S.: Disentangled representation for age-invariant face recognition: a mutual information minimization perspective. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3692–3701 (2021)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference Computer Vision Pattern Recognition, pp. 7132–7141 (2018)
Kanari, L., et al.: Objective morphological classification of neocortical pyramidal cells. Cereb. Cortex 29(4), 1719–1735 (2019)
Kasper, E.M., et al.: Pyramidal neurons in layer 5 of the rat visual cortex. II. Development of electrophysiological properties. J. Comp. Neurol. 339(4), 475–494 (1994)
Li, Z., et al.: Large-scale exploration of neuronal morphologies using deep learning and augmented reality. Neuroinformatics 16(3), 339–349 (2018)
Li, Z., et al.: Towards computational analytics of 3d neuron images using deep adversarial learning. Neurocomputing 438, 323–333 (2021)
Lin, X., Zheng, J.: A neuronal morphology classification approach based on locally cumulative connected deep neural networks. Appl. Sci. 9(18), 3876 (2019)
Lin, X., Zheng, J., Wang, X., Ma, H.: A neuronal morphology classification approach based on deep residual neural networks. In: Proceedings of the International Conference on Neural Information Processing, pp. 336–348 (2018)
Mihaljević, B., et al.: Comparing basal dendrite branches in human and mouse hippocampal ca1 pyramidal neurons with Bayesian networks. Sci. Rep. 10(1), 1–13 (2020)
Radenović, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1655–1668 (2018)
Schaefer, A.T., et al.: Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. J. Neurophysiol. 89(6), 3143–3154 (2003)
Spruston, N.: Pyramidal neurons: dendritic structure and synaptic integration. Nat. Rev. Neurosci. 9(3), 206–221 (2008)
Vasques, X., et al.: Morphological neuron classification using machine learning. Front. Neuroanat. 10, 102 (2016)
Wang, Y., et al.: A simplified morphological classification scheme for pyramidal cells in six layers of primary somatosensory cortex of juvenile rats. IBRO Reports 5, 74–90 (2018)
Zhang, T., et al.: Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks. Sci. Rep. 11(1), 1–14 (2021)
Zhang, Y., et al.: Pinpointing morphology and projection of excitatory neurons in mouse visual cortex. Front. Neurosci. 912 (2019)
Zhao, L., et al.: Learning view-disentangled human pose representation by contrastive cross-view mutual information maximization. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, pp. 12793–12802 (2021)
Zhou, M., et al.: Mutual information-driven pan-sharpening. In: Proceedings of the IEEE Conference Computer Vision Pattern Recognition, pp. 1798–1808 (2022)
Acknowledgments
This work was supported by the JKW Research Funds (20-163-14-LZ-001-004-01) and the Anhui Provincial Natural Science Foundation (2108085UD12). We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.
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Sun, C., Guo, Q., Yang, G., Zhao, F. (2023). Learning Task-Specific Morphological Representation for Pyramidal Cells via Mutual Information Minimization. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C., Zamzmi, G. (eds) Predictive Intelligence in Medicine. PRIME 2023. Lecture Notes in Computer Science, vol 14277. Springer, Cham. https://doi.org/10.1007/978-3-031-46005-0_12
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