Prior experience alters content-specific neural representations of visual input in frontoparietal and default-mode networks

为什么需要 RNN ？独特价值是什么？ 卷积神经网络 – CNN 已经很强大的，为什么还需要RNN？ 本文会用通俗易懂的方式来解释 RNN 的独特价值——处理序列数据。同

We have shown previously that our parameter-reduced variants of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) are comparable in performance to the standard LSTM RNN on the MNIST dataset. In this study, we show that this is also the case for two diverse benchmark datasets, namely, the review sentiment IMDB and the 20 Newsgroup datasets. Specifically, we focus on two of the simplest variants, namely LSTM_6 (i.e., standard LSTM with three constant fixed gates) and LSTM_C6 (i.e., LSTM_6 with fur

The whole history of mankind is the creation and improvement of tools. From the moment the ancient man took the first stick in his hands, the tools...

Ari Benjamin — how and why neural networks learn what they do. Neural network theory, continual learning, and computational neuroscience

Cross-posted from Microsoft Research Blog

Datenportal SNF-Kennzahlen Datengeschichten Projektsuche Datensätze Über das Datenportal DE FR EN SNF-Kennzahlen Datengeschichten Projektsuche Datensätze Über das Datenportal DE FR EN Closing the loop: The role of feedback in neural processing and perception 01.05.2025 – 31.07.2025 Zusammenfassung Wissenschaftliches Abstract Feedforward processing is very powerful. For example, convolutional neural networks (CNNs) achieve supra-human accuracy in tasks like object recognition using only a one-way

Sparsity is an issue in neural representation and we think it should be measured in artificial neural networks to understand how they are representing information at each layers. For example, are a few units doing the work or is there a distributed pattern across all units (i.e., overlapping units taking part in the representations of cat, car, etc.). So in What the Success of Brain Imaging Implies about the Neural Code we decided to use the Gini coefficient, inspired by its use in evaluating voxel activati

Collection of Neural Network Visualization Tools

田中専務 拓海先生、最近部下が『GeoHNN』という論文を推してきまして、現場に導入できるか迷っております。要…