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What is a Recurrent Neural Network or RNN, how it works, where it can be used? This article tries to answer the above questions. It also shows a demo implementation of a RNN used for a specific purpose, but you would be able to generalise it for your needs
The ability to analyze the brain's neural connectivity is emerging as a key foundation for brain-computer interface (BCI) technologies, such as controlling artificial limbs and enhancing human intelligence. To make these
The paper introduces Graph Transformer Networks, a novel method that dynamically learns meta-paths to enhance node representation in heterogeneous graphs
Uncovering the relationship between structure (connectivity) and function (neuronal activity) is a fundamental question across many areas of biology. However, investigating this directly in animal brains is challenging because of the immense complexity of their neural connections and the invasive surgeries that are typically needed. Lab-grown neurons with artificially controlled connections may become a useful alternative to animal testing, particularly as we learn how to accurately characterize their behav
Coherent activity in excitatory pulse-coupled networks From Scholarpedia Simona Olmi and Alessandro Torcini (2013), Scholarpedia, 8(10):30928. doi:10.4249/scholarpedia.30928 revision #143359 [ link to/cite this article ] Jump to: navigation , search Post-publication activity Curator: Simona Olmi Contributors: 1.00 - Alessandro Torcini 0.50 - Nick Orbeck Oleksandr V. Popovych Michael Rosenblum Thomas Kreuz Dr. Simona Olmi, Istituto dei Sistemi Complessi - CNR, Sesto Fiorentino, Firenze, Italy Alessandro Torc
I’m training a neural network to generate recipes based on a database of about 30,000 examples, using the open-source char-rnn framework. People have asked me whether I’ve tried making any of these recipes. The answer is no. No, I most definitely haven’t. I don’t know, in fact, of anyone who has dared
« Previous Page — Table of Contents — Next Page » A major limitation of many methods used for studying large-scale networks stems from the fact that the strength of ties is not taken into account. Granovetter (1973) argued that the strength of a social tie is a function of its duration, emotional intensity, intimacy
Select Page Neural representations of speech How are speech features represented and combined in the brain to form a unified meaning? Our research work aims to uncover the neural correlates of these representations, and the dynamical mechanisms by which they are transformed and integrated. Key publications: Neural manifolds carry reactivation of phonetic representations during semantic processing Interpretability of statistical approaches in speech and language neurosciecne Imagined speech can be decoded fr
Gerardo Duran-Martin cv articles posts # Posts bandits3 Bayes8 bayesian‑deep‑learning1 changepoint1 data‑analysis3 einsums2 ewma1 filtering5 frequentist1 gaussian‑processes1 HMM1 jax1 kalman‑filter3 linear‑algebra1 neural‑networks1 non‑stationary5 regimes3 sequential1 sequential‑decision‑making4 slides2 time‑series1 uncertainty6 ## 2026 A journey through a PhD 13 Apr 2026 Reflections as a PhD student in statistical machine learning. Streaming hidden Markov models 10 Apr 2026 A one