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40 papers · 1 filter
Improved Neuronal Ensemble Inference with Generative Model and MCMC
Shun Kimura, Keisuke Ota, Koujin Takeda
Neuronal ensemble inference is a significant problem in the study of biological neural networks. Various methods have been proposed for ensemble inference from experimental data of…
Statistical Neurodynamics of Deep Networks: Geometry of Signal Spaces
Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi
Statistical neurodynamics studies macroscopic behaviors of randomly connected neural networks. We consider a deep layered feedforward network where input signals are processed laye…
Discrete-attractor-like Tracking in Continuous Attractor Neural Networks
Chi Chung Alan Fung, Tomoki Fukai
Continuous attractor neural networks generate a set of smoothly connected attractor states. In memory systems of the brain, these attractor states may represent continuous pieces o…
Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks
Takashi Hayakawa, Tomoki Fukai
How the information microscopically processed by individual neurons is integrated and used in organizing the behavior of an animal is a central question in neuroscience. The cohere…
Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition
Haiping Huang, Taro Toyoizumi
Unsupervised neural network learning extracts hidden features from unlabeled training data. This is used as a pretraining step for further supervised learning in deep networks. Hen…
Spontaneous Motion on Two-dimensional Continuous Attractors
C. C. Alan Fung, S. -I. Amari
Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to…