As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

Rewards are crucial objects that induce learning, approach behavior, choices, and
emotions. Whereas emotions are difficult to investigate in animals, the learning function is …

A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well
established that it depends on pre-and postsynaptic activity. However, models that rely …

What happens in our brain when we make a decision? What triggers a neuron to send out a
signal? What is the neural code? This textbook for advanced undergraduate and beginning …

Most elementary behaviors such as moving the arm to grasp an object or walking into the
next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal …

Classical Hebbian learning puts the emphasis on joint pre-and postsynaptic activity, but
neglects the potential role of neuromodulators. Since neuromodulators convey information …

Basal forebrain cholinergic neurons constitute a major neuromodulatory system implicated
in normal cognition and neurodegenerative dementias. Cholinergic projections densely …

Reinforcement learning (RL) has recently regained popularity with major achievements such
as beating the European game of Go champion. Here, for the first time, we show that RL can …

In reward-based learning, synaptic modifications depend on a brief stimulus and a
temporally delayed reward, which poses the question of how synaptic activity patterns …