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Neuronal Plasticity

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Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning.

Proceedings of the National Academy of Sciences of the United States of America
Adolescent development is characterized by an improvement in multiple cognitive processes. While performance on cognitive operations improves during this period, the ability to learn new skills quickly, for example, a new language, decreases. During ...

Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing.

Nano letters
In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude hi...

Sequence learning, prediction, and replay in networks of spiking neurons.

PLoS computational biology
Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervi...

Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures.

ACS applied materials & interfaces
Brain-inspired intelligent systems demand diverse neuromorphic devices beyond simple functionalities. Merging biomimetic sensing with weight-updating capabilities in artificial synaptic devices represents one of the key research focuses. Here, we rep...

Contributions by metaplasticity to solving the Catastrophic Forgetting Problem.

Trends in neurosciences
Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequent...

Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Cell reports
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...

TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity.

IEEE transactions on biomedical circuits and systems
Human brain cortex acts as a rich inspiration source for constructing efficient artificial cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired computing paradigms for compact, fast and high-accuracy neuromorphic ha...

Continuous learning of spiking networks trained with local rules.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks (ANNs) experience catastrophic forgetting (CF) during sequential learning. In contrast, the brain can learn continuously without any signs of catastrophic forgetting. Spiking neural networks (SNNs) are the next generation o...

Forgetting memristor based STDP learning circuit for neural networks.

Neural networks : the official journal of the International Neural Network Society
The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. ...

Minicolumn-Based Episodic Memory Model With Spiking Neurons, Dendrites and Delays.

IEEE transactions on neural networks and learning systems
Episodic memory is fundamental to the brain's cognitive function, but how neuronal activity is temporally organized during its encoding and retrieval is still unknown. In this article, combining hippocampus structure with a spiking neural network (SN...