Jump to content Main menu Main menu move to sidebar hide Navigation Contribute Search Search Appearance Personal tools Contents move to sidebar hide (Top) 1 Architecture Toggle Architecture subsection 1.1 Convolutional layers 1.2 Pooling layers 1.3 Fully connected layers 1.4 Receptive field 1.5 Weights 1.6 Deconvolutional 2 History Toggle History subsection 2.1 Receptive fields in the visual cortex 2.2 Fukushima's analog threshold elements in a vision model 2.3 Neocognitron, origin of the trainable CNN arch
(2024) Elmoznino, Bonner. PLoS Computational Biology. Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core representational principles of computational models in neuroscience. Here we examined the geometry of DNN models of visual cortex by quantifying the laten
# Neural circuits Behavior By Sarah Thau 26 August 2026 ### Wouldn’t you like to know? A mouse would Mice seek information for curiosity’s sake—and their desire for knowledge versus a payout is represented distinctly in the brain, new findings suggest. Behavior ### Wouldn’t you like to know? A mouse would Mice seek information for curiosity’s sake—and their desire for knowledge versus a payout is represented distinctly in the brain, new findings suggest. By Sarah Thau 26 August 2026 | 5 min read
# Healing Trauma With Neural Annealing Andrés Gómez-Emilsson ../people/andrés-gómez-emilsson (Qualia Research Institute) https://www.qri.org/ May 8, 2021 - Appendix A ## Abstract Mystical-type experiences mediate the therapeutic benefit of psychedelic-assisted psychotherapy ( Griffiths et al. 2016 )( Ross et al. 2016 )( Yaden and Griffiths 2021 ). In this talk we will explore why this may be the case and how we might improve this effect. On the one hand we can interpret the effect of mystical-type expe
Ordinary differential equations (ODEs) are widely used to model complex dynamics that arises in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally very difficult. In this work, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary kernel method, and then feed them into a sparsely connected deep neural network with ReLU activation fu
Open Menu Search Login Home / Archives / Volume 2019, Issue 3 / Articles Make Some Noise. Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis Authors Jaehun Kim Delft University of Technology, Delft Stjepan Picek Delft University of Technology, Delft Annelie Heuser Univ Rennes, Inria, CNRS, IRISA Shivam Bhasin Physical Analysis and Cryptographic Engineering, Temasek Laboratories at Nanyang Technological University Alan Hanjalic Delft University of Technology, Delft DOI
I’ve been trying to apply Sparse Distributed Representations (SDR) to simple neural network layers. Can anyone suggest a method for achieving this? I want to ensure that only 2-5% of the neurons are active in each layer
A neural vocoder is a neural network designed to generate speech waveforms given acoustic features — often used as a backbone module for speech recognition tasks such as text-to-speech (TTS), speech-to-speech translation (S2ST), etc. Current neural vocoders however can struggle to maintain high sound quality without incurring high computational costs. In the new paper WaveFit
# Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights ## Abstract Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weights, facilitate calibrated reasoning about uncertainty, and allow inclusion of prior knowledge about the
Explore how synthetic data generation enhances neural network training by providing diverse, scalable datasets that improve model accuracy and robustness withou