Computational neuroscience is, to first order, dominated by two approaches: the 'bottom-
up'approach, which searches for statistical patterns in large-scale neural recordings, and the …

Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal
circuits by reversibly changing the effective strength of connections between neurons on …

Objective. The success of a cortical prosthetic device relies upon its ability to attain
resolvable spikes from many neurons in particular neural networks over long periods of time …

Neurons in parietal cortex exhibit task-related activity during decision-making tasks.
However, it remains unclear how long-term training to perform different tasks over months or …

Understanding learning through synaptic plasticity rules in the brain is a grand challenge for
neuroscience. Here we introduce a novel computational framework for inferring plasticity …

Working memory is closely involved in various cognitive activities, but its neural mechanism
is still under exploration. The mainstream view has long been that persistent activity is the …

Finding the form of synaptic plasticity is critical to understanding its functions underlying
learning and memory. We investigated an efficient method to infer synaptic plasticity rules in …

Information transmission in neural networks is influenced by both short-term synaptic
plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and …

Neurostimulation is a promising therapy for abating epileptic seizures. However, it is
extremely difficult to identify optimal stimulation patterns experimentally. In this study, human …

Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a
combination of both short-and long-term plasticity. Here we develop an extension of the …