Taro Toyoizumi

Neural Networks, January, 2024

Expansion of the editorial team.

[BibT_eX]

[DOI]

Neural Networks, 2024

A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness.

[BibT_eX]

[DOI]

Yuri Kinoshita

CoRR, 2024

2023

Progressive Interpretation Synthesis: Interpreting Task Solving by Quantifying Previously Used and Unused Information.

[BibT_eX]

[DOI]

Neural Comput., January, 2023

Another bumper year.

[BibT_eX]

[DOI]

Neural Networks, 2023

Announcement of the Neural Networks Best Paper Award.

[BibT_eX]

[DOI]

Neural Networks, 2023

Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Continual growth and a transition.

[BibT_eX]

[DOI]

Kenji Doya

Neural Networks, 2022

Spontaneous Emerging Preference in Two-tower Language Model.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Learning poly-synaptic paths with traveling waves.

[BibT_eX]

[DOI]

Yoshiki Ito

PLoS Comput. Biol., 2021

On the Achievability of Blind Source Separation for High-Dimensional Nonlinear Source Mixtures.

[BibT_eX]

[DOI]

Takuya Isomura

Neural Comput., 2021

Publisher Correction: Dimensionality reduction to maximize prediction generalization capability.

[BibT_eX]

[DOI]

Takuya Isomura

Nat. Mach. Intell., 2021

Dimensionality reduction to maximize prediction generalization capability.

[BibT_eX]

[DOI]

Takuya Isomura

Nat. Mach. Intell., 2021

An Information-theoretic Progressive Framework for Interpretation.

[BibT_eX]

[DOI]

CoRR, 2021

2019

Intrinsic Spine Dynamics Are Critical for Recurrent Network Learning in Models With and Without Autism Spectrum Disorder.

[BibT_eX]

[DOI]

Frontiers Comput. Neurosci., 2019

2018

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

[BibT_eX]

[DOI]

Christopher L. Buckley

PLoS Comput. Biol., 2018

2017

Reinforced backpropagation improves test performance of deep networks: a toy-model study.

[BibT_eX]

[DOI]

CoRR, 2017

Redundancy in active paths of deep networks: a random active path model.

[BibT_eX]

[DOI]

Alireza Goudarzi

CoRR, 2017

2016

Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials.

[BibT_eX]

[DOI]

Frontiers Comput. Neurosci., 2016

Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition.

[BibT_eX]

[DOI]

CoRR, 2016

2015

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2015

Advanced Mean Field Theory of Restricted Boltzmann Machine.

[BibT_eX]

[DOI]

CoRR, 2015

2013

Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian mixture Kalman filtering.

[BibT_eX]

[DOI]

Frontiers Comput. Neurosci., 2013

Blind Deconvolution of Hodgkin-Huxley neuronal model.

[BibT_eX]

[DOI]

Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

2012

Nearly Extensive Sequential Memory Lifetime Achieved by Coupled Nonlinear Neurons.

[BibT_eX]

[DOI]

Neural Comput., 2012

2009

Burst-Time-Dependent Plasticity Robustly Guides ON/OFF Segregation in the Lateral Geniculate Nucleus.

[BibT_eX]

[DOI]

Julijana Gjorgjieva

Stephen J. Eglen

PLoS Comput. Biol., 2009

Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness.

[BibT_eX]

[DOI]

Kamiar Rahnama Rad

Liam Paninski

Neural Comput., 2009

2007

Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution.

[BibT_eX]

[DOI]

Neural Comput., 2007

Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli.

[BibT_eX]

[DOI]

Yoshiyuki Sato

Kazuyuki Aihara

Neural Comput., 2007

2006

Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning.

[BibT_eX]

[DOI]

Neural Comput., 2006

Generalization of the Mean-Field Method for Power-Law Distributions.

[BibT_eX]

[DOI]