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Models, Neurological

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Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Journal of computational neuroscience
Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) ...

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.

Nature neuroscience
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computa...

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

International journal of neural systems
Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from hi...

Factorized visual representations in the primate visual system and deep neural networks.

eLife
Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many d...

A computationally efficient and robust looming perception model based on dynamic neural field.

Neural networks : the official journal of the International Neural Network Society
There are primarily two classes of bio-inspired looming perception visual systems. The first class employs hierarchical neural networks inspired by well-acknowledged anatomical pathways responsible for looming perception, and the second maps nonlinea...

Directly training temporal Spiking Neural Network with sparse surrogate gradient.

Neural networks : the official journal of the International Neural Network Society
Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features. However, the spiking all-or-none nature has prevented direct training of SNNs for various applications. The ...

Shared functional specialization in transformer-based language models and the human brain.

Nature communications
When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of nat...

Biological computation through recurrence.

Biochemical and biophysical research communications
One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial features of their environment, and use that information to compute the appropriate resp...

Leveraging spiking neural networks for topic modeling.

Neural networks : the official journal of the International Neural Network Society
This article investigates the application of spiking neural networks (SNNs) to the problem of topic modeling (TM): the identification of significant groups of words that represent human-understandable topics in large sets of documents. Our research i...

Shaping dynamical neural computations using spatiotemporal constraints.

Biochemical and biophysical research communications
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinar...