Explore structural, functional, and effective brain connectivity, their roles in health and disease, and advanced techniques for unraveling neural networks
Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity pattern is hippocampal replay, which is critical for memory consolidation. The switchings between replay events and a low-activity state in neural recordings suggests metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts f
## CONTEXT v4: Neural network code for text categorization in C++ on GPU Updated: March 29, 2019. Latest version: CONTEXT v4.00a (v4.00a (3/29/2019): makefile changed for newer GPUs; v4.00 (7/22/2017) ). GitHub What's new? v4 includes deep pyramid CNN (DPCNN) of [JZ17] . CONTEXT provides an implementation of the following types of neural network for text categorization: - Shallow CNN (convolutional neural networks) - [JZ15a] - Shallow CNN enhanced with unsupervised embeddings (embeddings trained in an uns
An engineer-friendly deep dive into how the human brain truly processes information, learns without backpropagation, and uses spiking neural networks, predictive coding, and dopamine-driven TD errors—challenging our notions of AI and matrix math
TheAILearner Mastering Artificial Intelligence Menu Skip to content EAT-NAS: Elastic Architecture Transfer for Neural Architecture Search Leave a reply Recently, Jiemin Fang et. al. , has published a paper that introduces a method to accelerate the neural architecture search named as “elastic architecture transfer for accelerating large-scale neural architecture search“. In this blog we will learn what is neural architecture search, what are the limitations associated with it and how this paper is
ACL Anthology About Announcements Communication channels Related work Copyright Credits Volunteer Development Feedback Using Citing papers Links in the Anthology Data access All FAQs Details Anthology identifiers Names ORCID iDs DOIs Verified authors Contributions Submissions Corrections Author pages Attachments GitHub Quantifying the Hyperparameter Sensitivity of Neural Networks for Character-level Sequence-to-Sequence Tasks Adam Wiemerslage , Kyle Gorman , Katharina von der Wense Correct Metadata for Use
tnet » Weighted Networks » Clustering A fundamental measure that has long received attention in both theoretical and empirical research is the clustering coefficient. This measure assesses the degree to which nodes tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups
Python, numpy, machine learning, AI, neural networks
Home Research Publications People Code/Resources Contact us Home Research Publications People Code/Resources Contact us See our Publications tab for a more comprehensive list of papers and preprints. Our research is supported by funding from the NIH (NIBIB and NIA) and NSF. Brain networks are typically modeled as interactions between neural elements. We have developed a framework for generating networks based on the interactivity of edges. This enables us to investigate circuit-level interactions with exqui
Posted on 7/11/2011 by RichardSocher # Interesting Neural Network Papers at ICML 2011 Maybe it’s too early to call, but with four separate Neural Network sessions at this year’s ICML , it looks like Neural Networks are making a comeback. Here are my highlights of these sessions. In general, my feeling is that these papers both demystify deep learning and show its broader applicability. The first observation I made is that the once disreputable “Neural” nomenclature is being used again in lieu of