Enhanced Preprints Neuroscience A theory and recipe to construct general and biologically plausible integrating continuous attractor neural networks Department of Brain and Cognitive Sciences & McGovern Institute, MIT, Cambridge, United States Integrative Computational Neuroscience Center and Yang-Tan Collective, MIT, Cambridge, United States https://doi.org/10.7554/eLife.107224.1 Reviewed Preprint v1July 28, 2025 Not revised Download Cite Share Share this article Close Cite this article Close Altmetric pro
In previous posts, I've discussed how we can train neural networks using backpropagation with gradient descent. One of the key hyperparameters to set in order to train a neural network is the learning rate for gradient descent
大トロ ml ・ design Mixture Density Networks June 14, 2015 For the Javascript demo of Mixture Density Networks, here is the link . Update: A more comprehensive write-up about MDNs implemented with TensorFlow here While I was going through Grave’s paper on artificial handwriting generation, I noticed that his model is not setup to predict the next location of the pen, but trained to generate a probability distribution of what happens next to the pen, including whether then pen gets lifted up. It
CSP Test --> Main Navigation ICLR Help/FAQ Contact ICLR Create Profile Code of Conduct Journal To Conference Track Diversity & Inclusion Proceedings at OpenReview Future Meetings Press Exhibitor Information ICLR Blog ICLR Twitter About ICLR Downloads Privacy Policy Reset Password My Stuff Login Select Year: (2025) 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 Poster GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks Dingyi Zhuang ⋅ Chonghe Jiang
Speaker–listener neural coupling underlies successful communication (2010) Greg J. Stephens Bias, belief, and consensus: Collective opinion formation on fluctuating networks C. elegans Tracking and Behavioral Measurement Lauren J. Silbert Topographic mapping of a hierarchy of temporal receptive windows using a narrated story On the same wavelength: predictable language enhances speaker-listener brain-to-brain synchrony in posterior superior temporal
The world is a chaotic and confusing place. Could advanced artificial intelligence help us make sense of it? Well, possibly, except that today’s “artificial intelligences” are not exactly what you’d call sophisticated. With a couple of hundred virtual neurons (as opposed to 16 billion neurons in the human brain), the neural networks I work with can only do limited, narrow tasks. Can they digest a list of CNN headlines and predict plausible new headlines based on what they’ve seen? No, but it’s
A few examples of feature visualization in convolutional neural networks with Tensorflow 2.0. In this part, we look at how much information is contained about the original image by trying to reconstruct the image based upon layer activations
Today, subsidies are the most common method of encouraging countries to deal with global warming; but it is debatable how effective they are. For instance, agriculture subsidies cause farmers to violate forest frontier and make them responsible for 14% of global deforestation every year. Not to mention excessive use of fertilizers degrades the soil and…
IOS Press Ebooks Guest Access ? Log in As a guest user you are not logged in or recognized by your IP address. You have access to the Front Matter, Abstracts, Author Index, Subject Index and the full text of Open Access publications. Search loading subjects... Learning Global Pairwise Interactions with Bayesian Neural Networks Authors Tianyu Cui, Pekka Marttinen, Samuel Kaski Pages 1087 - 1094 DOI 10.3233/FAIA200205 Category Research Article Series Frontiers in Artificial Intelligence and Applications Ebook