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Takuya Isomura
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2020 – today
- 2024
- [j13]Aswin Paul
, Takuya Isomura
, Adeel Razi
:
On Predictive Planning and Counterfactual Learning in Active Inference. Entropy 26(6): 484 (2024) - [i7]Aswin Paul, Takuya Isomura, Adeel Razi:
On Predictive planning and counterfactual learning in active inference. CoRR abs/2403.12417 (2024) - 2023
- [i6]Karl J. Friston, Tommaso Salvatori, Takuya Isomura, Alexander Tschantz, Alex B. Kiefer, Tim Verbelen, Magnus T. Koudahl, Aswin Paul, Thomas Parr, Adeel Razi, Brett Kagan, Christopher L. Buckley, Maxwell J. D. Ramstead:
Active Inference and Intentional Behaviour. CoRR abs/2312.07547 (2023) - 2021
- [j12]Takuya Isomura
, Taro Toyoizumi
:
Dimensionality reduction to maximize prediction generalization capability. Nat. Mach. Intell. 3(5): 434-446 (2021) - [j11]Takuya Isomura
, Taro Toyoizumi
:
Publisher Correction: Dimensionality reduction to maximize prediction generalization capability. Nat. Mach. Intell. 3(5): 457 (2021) - [j10]Takuya Isomura
, Taro Toyoizumi:
On the Achievability of Blind Source Separation for High-Dimensional Nonlinear Source Mixtures. Neural Comput. 33(6): 1433-1468 (2021) - [i5]Takuya Isomura:
Quadratic speedup of global search using a biased crossover of two good solutions. CoRR abs/2111.07680 (2021) - [i4]Manuel Baltieri, Takuya Isomura:
Kalman filters as the steady-state solution of gradient descent on variational free energy. CoRR abs/2111.10530 (2021) - 2020
- [j9]Takuya Isomura
, Karl J. Friston
:
Reverse-Engineering Neural Networks to Characterize Their Cost Functions. Neural Comput. 32(11): 2085-2121 (2020) - [j8]Yu Terada
, Tomoyuki Obuchi, Takuya Isomura
, Yoshiyuki Kabashima:
Inferring Neuronal Couplings From Spiking Data Using a Systematic Procedure With a Statistical Criterion. Neural Comput. 32(11): 2187-2211 (2020) - [i3]Takuya Isomura, Taro Toyoizumi:
Dimensionality reduction to maximize prediction generalization capability. CoRR abs/2003.00470 (2020)
2010 – 2019
- 2019
- [j7]Takuya Isomura
, Thomas Parr
, Karl J. Friston
:
Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence. Neural Comput. 31(12): 2390-2431 (2019) - [i2]Takuya Isomura:
Computational cost for determining an approximate global minimum using the selection and crossover algorithm. CoRR abs/1905.10017 (2019) - 2018
- [j6]Takuya Isomura
:
A Measure of Information Available for Inference. Entropy 20(7): 512 (2018) - [j5]Satoshi Kuroki, Takuya Isomura
:
Task-Related Synaptic Changes Localized to Small Neuronal Population in Recurrent Neural Network Cortical Models. Frontiers Comput. Neurosci. 12: 83 (2018) - [c1]Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima:
Objective and efficient inference for couplings in neuronal networks. NeurIPS 2018: 4976-4985 - [i1]Takuya Isomura, Taro Toyoizumi:
On the achievability of blind source separation for high-dimensional nonlinear source mixtures. CoRR abs/1808.00668 (2018) - 2017
- [j4]Yukimi Tanaka
, Takuya Isomura
, Kenta Shimba
, Kiyoshi Kotani, Yasuhiko Jimbo:
Neurogenesis Enhances Response Specificity to Spatial Pattern Stimulation in Hippocampal Cultures. IEEE Trans. Biomed. Eng. 64(11): 2555-2561 (2017) - 2016
- [j3]Takuya Isomura
, Koji Sakai, Kiyoshi Kotani, Yasuhiko Jimbo:
Linking Neuromodulated Spike-Timing Dependent Plasticity with the Free-Energy Principle. Neural Comput. 28(9): 1859-1888 (2016) - 2015
- [j2]Takuya Isomura
, Yutaro Ogawa, Kiyoshi Kotani, Yasuhiko Jimbo:
Accurate Connection Strength Estimation Based on Variational Bayes for Detecting Synaptic Plasticity. Neural Comput. 27(4): 819-844 (2015) - [j1]Takuya Isomura
, Kiyoshi Kotani, Yasuhiko Jimbo:
Cultured Cortical Neurons Can Perform Blind Source Separation According to the Free-Energy Principle. PLoS Comput. Biol. 11(12) (2015)
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