NeuroAI is the budding research field at the intersection of neuroscience and artificial intelligence. One of the core concepts used in the field is that artificial neural networks can act as good models of the brain. For example, it’s often claimed that convolutional neural networks can account for the response of the ventral visual stream
Neural network and deep learning introduction for those who skipped the math class but wants to follow the trend
# Neural Oscillations Integration: Discovering Micro Platform Frameworks Frequency : The rate at which the oscillation repeats (e.g., Periodic per second, or Hertz - Hz). Multiscale Modeling: Developing models that integrate information across different scales, from multiple neurons to large-scale brain networks. Schizophrenia : Deficits in gamma oscillations and altered alpha and beta activity are never reported, contributing to cognitive and perceptual impairments. These analytical methods, combined w
Modular neural networks outperform nonmodular neural networks on tasks ranging from visual question answering to robotics. These performance improvements are thought to be
Two theoretical studies reveal how networks of neurons may behave during reward-based learning
Frank Dieterle Ph. D. Thesis 2. Theory � Fundamentals of the Multivariate Data Analysis 2.7. Neural Networks � Universal Calibration Tools Home News About Me Ph. D. Thesis Abstract Table of Contents 1. Introduction 2. Theory � Fundamentals of the Multivariate Data Analysis 2.1. Overview of the Multivariate Quantitative Data Analysis 2.2. Experimental Design 2.3. Data Preprocessing 2.4. Data Splitting and Validation 2.5. Calibration of Linear Relationships 2.6. Calibration of Nonlinear Relationships 2
Slide 36 of 39 OOPS primitives for recurrent neural networks (Matteo Gagliolo, IDSIA, ongoing) RNN programming language: not based on stacks and traditional languages, but on matrix multiplications, simple local weight change algorithms, primitives for network growth etc.. --> Back to J. Schmidhuber 's OOPS page
As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two problems. 1. Due to a lack of architectural guidance, random recurrent connectivity is often adopted, which does not guarantee good performance. 2. Training of RSNNs is in general challenging, bottlenecking achievable model accuracy. To address these problems, we p
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Randomization for adversarial robustness: the Good, the Bad and the Ugly How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability → Multi-teacher knowledge distillation as an effective method for compressing ensembles of neural networks 投稿日: 2023年2月15日 作成者: jarxiv 要約 深層学習は、近年の人工知能の多くの成功に大きく貢献しています。 現在
--> Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be Valerio Biscione, Jeffrey S. Bowers. Year: 2021, Volume: 22 , Issue: 229, Pages: 1−28 Abstract When seeing a new object, humans can immediately recognize it across different retinal locations: the internal object representation is invariant to translation. It is commonly believed that Convolutional Neural Networks (CNNs) are architecturally invariant to translation thanks to the convolution and/or pooling