AIMC Topic: Memory

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Overcoming catastrophic forgetting in neural networks.

Proceedings of the National Academy of Sciences of the United States of America
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature ...

Dynamic neural architecture for social knowledge retrieval.

Proceedings of the National Academy of Sciences of the United States of America
Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often ...

Effects of the Red Bull energy drink on cognitive function and mood in healthy young volunteers.

Journal of psychopharmacology (Oxford, England)
The present study compared the cognitive and mood effects of two commercially available products, Red Bull energy drink 250 mL and Red Bull Sugarfree energy drink 250 mL, together with a matching placebo 250 mL. Twenty-four healthy young volunteers t...

Cognitive memory and mapping in a brain-like system for robotic navigation.

Neural networks : the official journal of the International Neural Network Society
Electrophysiological studies in animals may provide a great insight into developing brain-like models of spatial cognition for robots. These studies suggest that the spatial ability of animals requires proper functioning of the hippocampus and the en...

A simplified computational memory model from information processing.

Scientific reports
This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracti...

A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State.

Computational intelligence and neuroscience
Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a h...

Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays.

Computational intelligence and neuroscience
We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponenti...

A Possible Neural Representation of Mathematical Group Structures.

Bulletin of mathematical biology
Every cognitive activity has a neural representation in the brain. When humans deal with abstract mathematical structures, for instance finite groups, certain patterns of activity are occurring in the brain that constitute their neural representation...

Emergence of event cascades in inhomogeneous networks.

Scientific reports
There is a commonality among contagious diseases, tweets, and neuronal firings that past events facilitate the future occurrence of events. The spread of events has been extensively studied such that the systems exhibit catastrophic chain reactions i...

Maze learning by a hybrid brain-computer system.

Scientific reports
The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the co...