Adaptive behavior often requires predicting future events. The theory of reinforcement
learning prescribes what kinds of predictive representations are useful and how to compute …

Animal behavior is organized into nested temporal patterns that span multiple timescales.
This behavior hierarchy is believed to arise from a hierarchical neural architecture: Neurons …

Fiber photometry has become a popular technique to measure neural activity in vivo, but
common analysis strategies can reduce detection of effects because they condense within …

Reinforcement learning is the problem of predicting and maximizing long-term reward.
Computer scientists recognized that this problem could be solved by updating predictions …

What the basal ganglia do is an oft-asked question; answers range from the selection of
actions to the specification of movement to the estimation of time. Here I argue that how the …

Various mathematical models have been formulated to describe the changes in synaptic
strengths resulting from spike-timing-dependent plasticity (STDP). A subset of these models …

How external/internal 'state'is represented in the brain is crucial, since appropriate
representation enables goal-directed behavior. Recent studies suggest that state …

Neural networks, whether artificial in a computer or natural in the brain, could represent
information either using discrete symbols or continuous vector spaces. In this thesis, I …

How external/internal 'state'is represented in the brain is crucial, since appropriate
representation enables goal-directed behavior. Recent studies suggest that state …