AIMC Topic: Neurons

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Magnitude and angle dynamics in training single ReLU neurons.

Neural networks : the official journal of the International Neural Network Society
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...

Learning sequence attractors in recurrent networks with hidden neurons.

Neural networks : the official journal of the International Neural Network Society
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...

Bio-inspired computational memory model of the Hippocampus: An approach to a neuromorphic spike-based Content-Addressable Memory.

Neural networks : the official journal of the International Neural Network Society
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...

BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network.

Nature communications
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...

Persistent spiking activity in neuromorphic circuits incorporating post-inhibitory rebound excitation.

Journal of neural engineering
. 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...

Mechanical Neural Networks with Explicit and Robust Neurons.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Mechanical computing provides an information processing method to realize sensing-analyzing-actuation integrated mechanical intelligence and, when combined with neural networks, can be more efficient for data-rich cognitive tasks. The requirement of ...

A robust event-driven approach to always-on object recognition.

Neural networks : the official journal of the International Neural Network Society
We propose a neuromimetic architecture capable of always-on pattern recognition, i.e. at any time during processing. To achieve this, we have extended an existing event-based algorithm (Lagorce et al., 2017), which introduced novel spatio-temporal fe...

Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

PLoS computational biology
Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any sub...

Spiking generative adversarial network with attention scoring decoding.

Neural networks : the official journal of the International Neural Network Society
Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural n...

A 3D ray traced biological neural network learning model.

Nature communications
Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another datase...