AIMC Topic: Neuronal Plasticity

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Computational Modeling of Structural Synaptic Plasticity in Echo State Networks.

IEEE transactions on cybernetics Sep 19, 2022
Most existing studies on computational modeling of neural plasticity have focused on synaptic plasticity. However, regulation of the internal weights in the reservoir based on synaptic plasticity often results in unstable learning dynamics. In this a...
Neural Networks, Computer Neurons Nonlinear Dynamics Computer Simulation Neuronal Plasticity
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Continuous learning of spiking networks trained with local rules.

Neural networks : the official journal of the International Neural Network Society Sep 7, 2022
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...
Education, Continuing Neuronal Plasticity Synapses Neural Networks, Computer Brain
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Overcoming Long-Term Catastrophic Forgetting Through Adversarial Neural Pruning and Synaptic Consolidation.

IEEE transactions on neural networks and learning systems Aug 31, 2022
Enabling a neural network to sequentially learn multiple tasks is of great significance for expanding the applicability of neural networks in real-world applications. However, artificial neural networks face the well-known problem of catastrophic for...
Neural Networks, Computer Machine Learning Neuronal Plasticity Mammals Learning Brain Animals
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Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures.

ACS applied materials & interfaces Jul 26, 2022
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...
Learning Neuronal Plasticity Biomimetics Synapses Neural Networks, Computer
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Contributions by metaplasticity to solving the Catastrophic Forgetting Problem.

Trends in neurosciences Jul 4, 2022
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...
Brain Learning Humans Neural Networks, Computer Neuronal Plasticity Neurobiology
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Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing.

Nano letters Jun 23, 2022
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...
Artificial Intelligence Neuronal Plasticity Brain Synapses Wearable Electronic Devices
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Sequence learning, prediction, and replay in networks of spiking neurons.

PLoS computational biology Jun 21, 2022
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...
Synapses Neurons Neural Networks, Computer Learning Neuronal Plasticity Models, Neurological
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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 May 27, 2022
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 ...
Reinforcement, Psychology Adolescent Neuronal Plasticity Adolescent Development Models, Neurological Humans Synapses Memory, Short-Term Neural Networks, Computer Prefrontal Cortex
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Multiwavelength Optoelectronic Synapse with 2D Materials for Mixed-Color Pattern Recognition.

ACS nano May 25, 2022
Neuromorphic visual systems emulating biological retina functionalities have enormous potential for in-sensor computing, with prospects of making artificial intelligence ubiquitous. Conventionally, visual information is captured by an image sensor, s...
Synapses Neural Networks, Computer Artificial Intelligence Algorithms Neuronal Plasticity
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Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks.

IEEE transactions on neural networks and learning systems May 2, 2022
Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit information, which are not only biologically realistic but also suitable for ultralow-power event-driven neuromorphic implementation. Just like other deep lear...
Neuronal Plasticity Neurons Synaptic Potentials Neural Networks, Computer Algorithms
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