Publications

Preprints

• 1. Isomura, T., Shimazaki, H. & Friston, K. Canonical neural networks perform active inference. bioRxiv:2020.12.10.420547 (2020).

1. Isomura, T. Computational cost for determining an approximate global minimum using the selection and crossover algorithm. arXiv:1905.10017 (2019).

2. Torigoe, M., Islam, T., Kakinuma, H., Fung, C.C.A., Isomura, T., Shimazaki, H., Aoki, T., Fukai, T. & Okamoto, H. Future State Prediction Errors Guide Active Avoidance Behavior by Adult Zebrafish. bioRxiv:546440 (2019).

Research papers

1. Isomura, T. & Toyoizumi, T. Dimensionality reduction to maximize prediction generalization capability. Nature Machine Intelligence 3:434-446 (2021).

2. Isomura, T. & Toyoizumi, T. On the achievability of blind source separation for high-dimensional nonlinear source mixtures. Neural Computation 33:1433-1468 (2021).

3. Isomura, T. & Friston, K. Reverse-engineering neural networks to characterize their cost functions. Neural Computation 32:2085-2121 (2020).

4. Terada, Y., Obuchi, T., Isomura, T. & Kabashima, Y. Inferring neuronal couplings from spiking data using a systematic procedure with a statistical criterion. Neural Computation 32: 2187-2211 (2020).

   1. Isomura, T., Parr, T. & Friston, K. Bayesian filtering with multiple internal models – towards a theory of social intelligence. Neural Computation 31: 2390-2431 (2019).

   2. Isomura, T. & Toyoizumi, T. Multi-context blind source separation by error-gated Hebbian rule. Scientific Reports 9:7127 (2019).

   3. Palacios, E.R., Isomura, T., Parr, T. & Friston, K. The emergence of synchrony in networks of mutually inferring neurons. Scientific Reports 9:6412 (2019).

   4. Yamaguchi, I., Isomura, T., Nakao, H., Ogawa, Y., Jimbo, Y. & Kotani, K. Suppression of macroscopic oscillations in mixed populations of active and inactive oscillators coupled through lattice Laplacian. Journal of the Physical Society of Japan 88(5):054004 (2019).

   5. Isomura, T. & Friston, K. In vitro neural networks minimise variational free energy. Scientific Reports 8:16926 (2018).

   6. Isomura, T. A measure of information available for inference. Entropy 20(7):512 (2018).

   7. Isomura, T. & Toyoizumi, T. Error-gated Hebbian rule: a local learning rule for principal and independent component analysis. Scientific Reports 8:1835 (2018).

   8. Kuroki, S. & Isomura, T. Task-related synaptic changes localized to small neuronal population in recurrent neural network cortical models. Frontiers in Computational Neuroscience 12:83 (2018).

   9. Terada, Y., Obuchi, T., Isomura, T. & Kabashima, Y. Objective and efficient inference for couplings in neuronal networks. Advances in Neural Information Processing Systems 32:4976-4985 (2018).

   10. Ciba, M., Isomura, T., Jimbo, Y., Bahmer, A. & Thielemann, C. Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony. Journal of Neuroscience Methods 293:136-143 (2018).

   11. Tanaka, Y., Isomura, T., Shimba, K., Kotani, K. & Jimbo, Y. Neurogenesis enhances response specificity to spatial pattern stimulation in hippocampal cultures. IEEE Transactions on Biomedical Engineering 64(11):2555-2561 (2017).

   12. Kondo, Y., Yada, Y., Haga, T., Takayama, Y., Isomura, T., Jimbo, Y., Fukayama, O., Hoshino, T. & Mabuchi, K. Temporal relation between neural activity and neurite pruning on a numerical model and a microchannel device with micro electrode array. Biochemical and Biophysical Research Communications 486(2):539-544 (2017).

   13. Isomura, T., Sakai, K., Kotani, K. & Jimbo, Y. Linking neuromodulated spike-timing dependent plasticity with the free-energy principle. Neural Computation 28(9):1859-1888 (2016).

   14. Isomura, T. & Toyoizumi, T. A local learning rule for independent component analysis. Scientific Reports 6:28073 (2016).

   15. 関本正志, 下野勇希, 赤尾旭彦, 磯村拓哉, 小川雄太郎, 黄琦, 小谷潔, 神保泰彦. 視覚と聴覚による多感覚拡張現実感技術を用いた Brain-Computer Interface の開発. 電気学会論文誌C, 136(9):1291-1297 (2016).

   16. Isomura, T., Kotani, K. & Jimbo, Y. Cultured cortical neurons can perform blind source separation according to the free-energy principle. PLoS Computational Biology 11(12):e1004643 (2015).

   17. Isomura, T., Shimba, K., Takayama, Y., Takeuchi, A., Kotani, K. & Jimbo, Y. Signal transfer within a cultured asymmetric cortical neuron circuit. Journal of Neural Engineering 12(6):066023 (2015).

   18. Isomura, T., Ogawa, Y., Kotani, K. & Jimbo, Y. Accurate connection strength estimation based on variational Bayes for detecting synaptic plasticity. Neural Computation 27(4):819-844 (2015).

   19. Shimba, K., Sakai, K., Isomura, T., Kotani, K. & Jimbo, Y. Axonal conduction slowing induced by spontaneous bursting activity in cortical neurons cultured in a microtunnel device. Integrative Biology 7(1):64-72 (2015).

   20. 田中幸美, 門倉智之助, 磯村拓哉, 榛葉健太, 小谷潔, 神保泰彦. 培養海馬ニューロン新生の制御によるパターン分離能力の向上. 電気学会論文誌C, 135(7):805-812 (2015).

   21. 磯村拓哉, 武内彬正, 榛葉健太, 小谷潔, 神保泰彦. 結合強度推定に基づくニューロン種の分類–ドーパミンニューロンを含む神経回路網の電気活動–. 電気学会論文誌C, 133(9):1806-1813 (2013).

   22. 榛葉健太, 有松和之, 磯村拓哉, 武内彬正, 小谷潔, 神保泰彦. セロトニンによる培養神経回路網の発火パターン調節. 電気学会論文誌C, 133(9):1814-1819 (2013).

   23. 磯村拓哉, 武内彬正, 榛葉健太, 小谷潔, 神保泰彦. Izhikevichモデルを用いた培養神経回路網の結合強度推定. 電気学会論文誌C, 132(10):1581-1588 (2012).

   24. 磯村拓哉. 培養神経回路網における可塑性の統計推定. ライフサポート, 24(1), 10 (2012).

Reviews

1. 磯村拓哉. 自由エネルギー原理の解説：知覚・行動・他者の思考の推論. 日本神経回路学会誌 25(3):71-85 (2018).

2. Kuśmierz, Ł., Isomura, T. & Toyoizumi, T. Learning with three factors: modulating Hebbian plasticity with errors. Current Opinion in Neurobiology 46:170-177 (2017).

Proceedings

1. Isomura T, Kotani K, Jimbo Y. Maximum Entropy Learning in Cultured Cortical Neural Networks. Joint 8th Int Conf on Soft Comput and Int Sys and 17th Int Symp on Adv Int Sys (SCIS&ISIS2016), 849.01 (2016).

   1. Isomura T, Kotani K, Jimbo Y. Neuronal Maximum a Posteriori Estimation on Microelectrode Arrays. Proceedings of MEA Meeting 2016, doi: 10.3389/conf.fnins.2016.93.00120 (2016).

   2. Tanaka Y, Isomura T, Shimba K, Kotani K, Jimbo Y. Distance Dependent Activation of Dissociated Hippocampal Network by Tetanic Stimulation. 37th Ann Int Conf IEEE EMBS (EMBS2015), FrFPoT3.17 (2015).

   3. Isomura T, Kotani K, Jimbo Y. Cultured Cortical Neurons Can Separate Source Signals From Mixture Inputs. Proceedings of MEA Meeting 2014, 183–184 (2014).

   4. Isomura T, Kotani K, Jimbo Y. STDP with spiking neuron model can find first principal component of multiple inputs: an approach using Fokker-Planck equation. 35th Ann Conf of IEEE Eng in Med and Bio Soc, SaD03.14 (2013).

   5. Arimatsu K, Isomura T, Shimba K, Kotani K, Jimbo Y. Bursting Activity of Neuronal Network Is Modified in Response to Burst-Like Pattern Stimulation. 35th Ann Conf of IEEE Eng in Med and Bio Soc, 3231 (2013).

Invited talks

1. 磯村拓哉. フリーエナジークリーチャーズ〜目指せベイズ最適〜. 日本神経回路学会 オータムスクールASCONE2019『脳科学と数理の未来』, 諏訪, 2019年11月.

2. Isomura, T. Biological plausibility of variational free energy as a cost function for neural networks. 生理研研究会2019 認知神経科学の先端「脳の理論から身体・世界へ」, 岡崎, 2019年9月2日.

3. 磯村拓哉. わかりやすい自由エネルギー原理. 第42回日本神経科学大会(Neuro2019), 教育講演3EL07m, 新潟, 2019年7月27日.

4. Isomura, T. Predicting concise hidden-state dynamics with an accuracy guarantee. Computational Neuroscience Conference Workshop, Barcelona, 16 July 2019.

5. Isomura, T. Possible implementations of the free-energy principle in biological neural networks to optimize inference and prediction. Society for the Neural Control of Motion, Workshop, Toyama, 23 April 2019.

6. Isomura T. The free-energy principle in biological neural networks. Research Seminar, University of Sussex (Brighton, UK), 9 March 2018.

7. 磯村拓哉. 培養神経回路網における内部モデルと自由エネルギー原理：電気生理と学習理論. 第27回日本数理生物学会年会, S04-2, 札幌, 2017年10月6日.

8. 磯村拓哉. 神経回路網における自由エネルギー原理 –電気生理実験による検証と生物学的な学習アルゴリズムの開発–. 日本神経回路学会 時限研究会2017, 京都, 2017年8月25日.

9. Isomura T. Kotani K, Jimbo Y. Maximum entropy learning in cultured cortical neural networks. Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2016), Hokkai-Gakuen University (Sapporo, Hokkaido), 27 August 2016.

10. Isomura T. Cultured cortical neurons can perform blind source separation according to the free-energy principle. Consciousness Club Tokyo, The University of Tokyo (Komaba, Tokyo), 31 March 2016.

11. Isomura T. Introduction of the free-energy principle. Workshop, National Institute of Advanced Industrial Science and Technology (AIST) (Aomi, Tokyo), 24 November 2015.