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Neurons

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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...

Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences.

Scientific reports
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...

Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference.

Scientific reports
3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods...

A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot.

Biochemical and biophysical research communications
To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relationship between them. Here we use light sheet fluorescence microscope (LSFM) combined with microfluidi...

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...

BiƓmuS: 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...