近年の観測技術の発展によって、脳科学の実験研究は目覚ましい進歩を遂げています。しかし、脳の情報処理メカニズムは未だに多くの部分が謎に包まれています。今後は理論研究が実験研究と並ぶ主軸となって脳科学分野を牽引していくことが期待されています。そこで、本講義では物理学の手法を用いて、脳の情報処理機構を理論的に研究した試みをご説明します。統計力学 [1, 2]、情報理論 [3, 4]、動力学 [5, 6] などの考え方は脳科学分野の新たな仮説を提唱したり、脳の情報処理からヒントを得てそれを応用したりする上で有用です。以下ではこのようなアプローチで脳科学の諸問題を取り扱った例を幾つかご紹介していきます。
[1] Taro Toyoizumi and LF Abbott. Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime. Physical Review E, 84(5):051908, 2011.
[2] Taro Toyoizumi and Haiping Huang. Structure of attractors in randomly connected net- works. Physical Review E, 91(3):032802, 2015.
[3] Taro Toyoizumi, Jean-Pascal Pfister, Kazuyuki Aihara, and Wulfram Gerstner. Generalized bienenstock–cooper–munro rule for spiking neurons that maximizes information transmis- sion. Proceedings of the National Academy of Sciences, 102(14):5239–5244, 2005.
[4] Zhengqi He and Taro Toyoizumi. An information-theoretic progressive framework for in- terpretation. arXiv preprint arXiv:2101.02879, 2021.
[5] Satohiro Tajima, Toru Yanagawa, Naotaka Fujii, and Taro Toyoizumi. Untangling brain- wide dynamics in consciousness by cross-embedding. PLoS Computational Biology, 11(11): e1004537, 2015.
[6] Christopher L Buckley and Taro Toyoizumi. A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback. PLoS Computational Biology, 14(1):e1005926, 2018.
[7] John M Beggs and Dietmar Plenz. Neuronal avalanches in neocortical circuits. Journal of Neuroscience, 23(35):11167–11177, 2003.
[8] Haim Sompolinsky, Andrea Crisanti, and Hans-Jurgen Sommers. Chaos in random neural networks. Physical Review Letters, 61(3):259, 1988.
[9] L- ukasz Ku´smierz, Shun Ogawa, and Taro Toyoizumi. Edge of chaos and avalanches in neural networks with heavy-tailed synaptic weight distribution. Physical Review Letters, 125(2):028101, 2020.
[10] Patrick Haggard, Sam Clark, and Jeri Kalogeras. Voluntary action and conscious awareness. Nature Neuroscience, 5(4):382–385, 2002.
[11] Noham Wolpe, Patrick Haggard, Hartwig R Siebner, and James B Rowe. Cue integration and the perception of action in intentional binding. Experimental Brain Research, 229(3): 467–474, 2013.
[12] Yoshiyuki Sato, Taro Toyoizumi, and Kazuyuki Aihara. Bayesian inference explains percep- tion of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli. Neural Computation, 19(12):3335–3355, 2007.
[13] Roberto Legaspi and Taro Toyoizumi. A bayesian psychophysics model of sense of agency. Nature Communications, 10(1):1–11, 2019.
[14] Aapo Hyv¨arinen and Erkki Oja. Independent component analysis: algorithms and appli- cations. Neural Networks, 13(4-5):411–430, 2000.
[15] L- ukasz Ku´smierz, Takuya Isomura, and Taro Toyoizumi. Learning with three factors: modulating hebbian plasticity with errors. Current Opinion in Neurobiology, 46:170–177, 2017.
[16] Takuya Isomura and Taro Toyoizumi. A local learning rule for independent component analysis. Scientific Reports, 6(1):1–17, 2016.
[17] Mohammed E Fouda, Emre Neftci, Ahmed Eltawil, and Fadi Kurdahi. Independent com- ponent analysis using rrams. IEEE Transactions on Nanotechnology, 18:611–615, 2018.
[18] Takuya Isomura and Taro Toyoizumi. Error-gated hebbian rule: A local learning rule for principal and independent component analysis. Scientific Reports, 8(1):1–11, 2018.
[19] Takuya Isomura and Taro Toyoizumi. Multi-context blind source separation by error-gated hebbian rule. Scientific Reports, 9(1):1–13, 2019.
[20] Takuya Isomura and Taro Toyoizumi. Dimensionality reduction to maximize prediction generalization capability. Nature Machine Intelligence, pages 1–13, 2021.
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