AIMC Topic: Memory

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Design and Implementation of Pavlovian Associative Memory Based on DNA Neurons.

IEEE transactions on neural networks and learning systems
In the field of biocomputing and neural networks, deoxyribonucleic acid (DNA) strand displacement (DSD) technology performs well in computation, programming, and information processing. In this article, the multiplication gate, addition gate, and thr...

Scalable Multi-FPGA HPC Architecture for Associative Memory System.

IEEE transactions on biomedical circuits and systems
Associative memory is a cornerstone of cognitive intelligence within the human brain. The Bayesian confidence propagation neural network (BCPNN), a cortex-inspired model with high biological plausibility, has proven effective in emulating high-level ...

Studying memory narratives with natural language processing.

Trends in cognitive sciences
Cognitive neuroscience research has begun to use natural language processing (NLP) to examine memory narratives with the hopes of gaining a nuanced understanding of the mechanisms underlying differences in memory recall, both across groups and tasks....

A general framework for interpretable neural learning based on local information-theoretic goal functions.

Proceedings of the National Academy of Sciences of the United States of America
Despite the impressive performance of biological and artificial networks, an intuitive understanding of how their local learning dynamics contribute to network-level task solutions remains a challenge to this date. Efforts to bring learning to a more...

Temporal Contrastive Learning through implicit non-equilibrium memory.

Nature communications
The backpropagation method has enabled transformative uses of neural networks. Alternatively, for energy-based models, local learning methods involving only nearby neurons offer benefits in terms of decentralized training, and allow for the possibili...

From materials to applications: a review of research on artificial olfactory memory.

Materials horizons
Olfactory memory forms the basis for biological perception and environmental adaptation. Advancing artificial intelligence to replicate this biological perception as artificial olfactory memory is essential. The widespread use of various robotic syst...

Memristor-based circuit design of interweaving mechanism of emotional memory in a hippocamp-brain emotion learning model.

Neural networks : the official journal of the International Neural Network Society
Endowing robots with human-like emotional and cognitive abilities has garnered widespread attention, driving deep investigations into the complexities of these processes. However, few studies have examined the intricate circuits that govern the inter...

Tuned Compositional Feature Replays for Efficient Stream Learning.

IEEE transactions on neural networks and learning systems
Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning to classify objects by training on nonrepeating video frames in temp...

Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic hardware systems. While there has been substantial progress in SNN research, artificial SNNs still lack many capabilities of their biological counterparts. In biologi...

Hybrid neural networks for continual learning inspired by corticohippocampal circuits.

Nature communications
Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal...