Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

For many years, neuroscientists have investigated the behavioural, computational and
neurobiological mechanisms that support value-based decisions, revealing how humans …

Biological neural networks do not only include long-term memory and weight multiplication
capabilities, as commonly assumed in artificial neural networks, but also more complex …

The predictive nature of the hippocampus is thought to be useful for memory-guided
cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been …

Considering the individual differences between neurons, the fractional-order framework is
introduced, and the neurons with various orders denote the individual differences during the …

In neural circuits, synaptic strengths influence neuronal activity by sha** network
dynamics, and neuronal activity influences synaptic strengths through activity-dependent …

Working memory has long been thought to arise from sustained spiking/attractor dynamics.
However, recent work has suggested that short-term synaptic plasticity (STSP) may help …

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 …

There is substantial experimental evidence that learning and memory-related behaviours
rely on local synaptic changes, but the search for distinct plasticity rules has been driven by …

Primates and other animals must detect novel objects. However, the neuronal mechanisms
of novelty detection remain unclear. Prominent theories propose that visual object novelty is …