AIMC Topic: Neurons

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A lightweight and gradient-stable neural layer.

Neural networks : the official journal of the International Neural Network Society
To enhance resource efficiency and model deployability of neural networks, we propose a neural-layer architecture based on Householder weighting and absolute-value activating, called Householder-absolute neural layer or simply Han-layer. Compared to ...

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Journal of neural engineering
Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different object...

Functional loops: Monitoring functional organization of deep neural networks using algebraic topology.

Neural networks : the official journal of the International Neural Network Society
Various topological methods have emerged in recent years to investigate the inner workings of deep neural networks (DNNs) based on the structural and weight information. However, their effectiveness is restricted due to the stratified structure and v...

A universal ANN-to-SNN framework for achieving high accuracy and low latency deep Spiking Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) have become one of the most prominent next-generation computational models owing to their biological plausibility, low power consumption, and the potential for neuromorphic hardware implementation. Among the various met...

Bayesian inference is facilitated by modular neural networks with different time scales.

PLoS computational biology
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampli...

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Advanced materials (Deerfield Beach, Fla.)
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, mach...

Modular Spiking Neural Membrane Systems for Image Classification.

International journal of neural systems
A variant of membrane computing models called Spiking Neural P systems (SNP systems) closely mimics the structure and behavior of biological neurons. As third-generation neural networks, SNP systems have flexible architectures allowing the design of ...

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

Sensors (Basel, Switzerland)
A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initial...

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

ACS applied materials & interfaces
Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic...

Unsupervised learning of perceptual feature combinations.

PLoS computational biology
In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combination...