研究室で過去に発表したもののだいぶ改訂版です． - Download as a PDF, PPTX or view online for free

Spiking Neural Networks (SNNs) are brain-inspired, event-driven machine learning algorithms that have been widely recognized in producing ultra-high-energy-efficient hardware. Among existing SNNs, unsupervised SNNs based on synaptic plasticity, especially Spike-Timing-Dependent Plasticity (STDP), are considered to have great potential in imitating the learning process of the biological brain. Nevertheless, the existing STDP-based SNNs have limitations in constrained learning capability and/or slow learning

Human sensory systems are very good at recognizing objects that we see or words that we hear, even if the object is upside down or the word is spoken by a voice we've never heard.

Read articles about Convolutional Neural Net on Towards Data Science - the world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals

This explores the specific places where bigger models and more data still don't deliver true compositional generalization — combining known pieces in new ways — and why scaling papers over the gap rat

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech…

Rewiring-induced Synchronization and Chaos in Pulse-coupled Neural Networks --> After downloading firingviewer.jar , please execute it by double-clicking, or typing "java -jar firingviewer.jar". If the above application does not start, please install OpenJDK from adoptium.net . Explanation of Applet Firing of neurons On the 100x100 two-dimensional grid, an excitatory neuron (E) and an inhibitory neuron (I) are placed. The firings of excitatory neurons and inhibitory neurons are shown by yellow dots and blue

Skip to main content Home About Submissions Content Research Integrity Open navigation Home About Submissions Content Research Integrity Account Computational Psychiatry Start Submission Become a Reviewer Reading: Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study Download Alt. Display Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study Research Articles Authors Brian Zaboski Sara

Rigorous treatment of RNNs: hidden state dynamics, vanishing and exploding gradients via weight matrix eigenvalues, LSTM gating, and comparison to transformers.

Abstract page for arXiv paper 2606.00243: Dynamics and Representation Structure of Local Approximations to Gradient-Based Learning in Linear Recurrent Neural Networks