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

Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field

Groups of neurons in the human brain produce patterns of activity that represent information about the stimuli that one is perceiving and then convey these patterns to different brain regions via nerve cell junctions known as synapses. So far, most neuroscience studies have focused on the two primary components of neuron information processing individually (i.e., the representation of stimuli in the form of neural activity and the transmission of this information in networks that model neural interactions

How to Effectively Implement Dropout Regularization Implementing dropout regularization requires careful consideration of its placement and rate.

↓ Skip to main content Altmetric What is this page? Embed badge Share Neural Representations of Natural Language Overview of attention for book Table of Contents Altmetric Badge Book Overview Altmetric Badge Chapter 1 Introduction to Neural Networks for Machine Learning Altmetric Badge Chapter 2 Recurrent Neural Networks for Sequential Processing Altmetric Badge Chapter 3 Word Representations Altmetric Badge Chapter 4 Word Sense Representations Altmetric Badge Chapter 5 Sentence Representations and Beyond

Hello Fellows I wanted to understand HTM better. So I searched information to compare HTM and neural networks (deep learning) on building artificial intelligence. Which one is better and why? Listing below is what I en

Modern pattern recognition methods are based on convolutional networks since they are able to learn complex patterns that benefit the classification. However, convolutional networks are computationally expensive and require a considerable amount of memory, which limits their deployment on low-power and resource-constrained systems. To handle these problems, recent approaches have proposed pruning strategies that find and remove unimportant neurons (i.e., filters) in these networks. Despite achieving remarka