Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing th...
Proceedings of the National Academy of Sciences of the United States of America
Jun 24, 2024
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribu...
Neural networks : the official journal of the International Neural Network Society
Jun 22, 2024
Understanding the training dynamics of deep ReLU networks is a significant area of interest in deep learning. However, there remains a lack of complete elucidation regarding the weight vector dynamics, even for single ReLU neurons. To bridge this gap...
Neural networks : the official journal of the International Neural Network Society
Jun 22, 2024
The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store pr...
Neural networks : the official journal of the International Neural Network Society
Jun 21, 2024
Convergence in the presence of multiple equilibrium points is one of the most fundamental dynamical properties of a neural network (NN). Goal of the paper is to investigate convergence for the classic Brain-State-in-a-Box (BSB) NN model and some of i...
Neural networks : the official journal of the International Neural Network Society
Jun 20, 2024
The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating s...
Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders rem...
. This study introduces a novel approach for integrating the post-inhibitory rebound excitation (PIRE) phenomenon into a neuronal circuit. Excitatory and inhibitory synapses are designed to establish a connection between two hardware neurons, effecti...
Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation pro...
Neural networks : the official journal of the International Neural Network Society
Jun 19, 2024
Spiking neural networks (SNNs) have attracted attention due to their biological plausibility and the potential for low-energy applications on neuromorphic hardware. Two mainstream approaches are commonly used to obtain SNNs, i.e., ANN-to-SNN conversi...