Neuromorphic hardware enables fast and power-efficient neural network–based artificial
intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be …

Notions of surprise and novelty have been used in various experimental and theoretical
studies across multiple brain areas and species. However,'surprise'and 'novelty'refer to …

Surprising events trigger measurable brain activity and influence human behavior by
affecting learning, memory, and decision-making. Currently there is, however, no consensus …

Learning in the brain is poorly understood and learning rules that respect biological
constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose …

A key feature of sequential decision making under uncertainty is a need to balance between
exploiting—choosing the best action according to the current knowledge, and exploring …

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence
of external reward or when the environment changes. Here, we employ a deep sequential …

Learning in a stochastic and changing environment is a difficult task. Models of learning
typically postulate that observations that deviate from the learned predictions are surprising …

Learning how to reach a reward over long series of actions is a remarkable capability of
humans, and potentially guided by multiple parallel learning modules. Current brain imaging …

Sequential decision problems distill important challenges frequently faced by humans.
Through repeated interactions with an uncertain world, unknown statistics need to be …

When a neuron is driven beyond its threshold, it spikes. The fact that it does not
communicate its continuous membrane potential is usually seen as a computational liability …