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Memory

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Computational models of Idling brain activity for memory processing.

Neuroscience research
Studying the underlying neural mechanisms of cognitive functions of the brain is one of the central questions in modern biology. Moreover, it has significantly impacted the development of novel technologies in artificial intelligence. Spontaneous act...

Coherent noise enables probabilistic sequence replay in spiking neuronal networks.

PLoS computational biology
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type o...

Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies.

Neural computation
Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learni...

Unavoidable social contagion of false memory from robots to humans.

The American psychologist
Many of us interact with voice- or text-based conversational agents daily, but these conversational agents may unintentionally retrieve misinformation from human knowledge databases, confabulate responses on their own, or purposefully spread disinfor...

Predicting memorability of face photographs with deep neural networks.

Scientific reports
With the advent of social media in our daily life, we are exposed to a plethora of images, particularly face photographs, every day. Recent behavioural studies have shown that some of these photographs stick in the mind better than others. Previous r...

In Silico Clinical Trials: Is It Possible?

Methods in molecular biology (Clifton, N.J.)
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becom...

Lifelong Learning With Cycle Memory Networks.

IEEE transactions on neural networks and learning systems
Learning from a sequence of tasks for a lifetime is essential for an agent toward artificial general intelligence. Despite the explosion of this research field in recent years, most work focuses on the well-known catastrophic forgetting issue. In con...

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network.

IEEE transactions on neural networks and learning systems
Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific...

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...