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Revealing the structure of pharmacobehavioral space through motion sequencing. Nat Neurosci.

Standard behavioral assays that are used for screening candidate therapeutic drugs, such as the elevated plus maze and forced swim test, yield only one or two dimensions of information. Wiltschko et al. used a machine learning algorithm called MoSeq (for motion sequencing) to analyze 3D video recordings of twenty minutes of open field mouse activity, thereby obtaining a much higher dimensional readout of behavior in response to a wide variety of psychoactive drugs and doses. [Read More]

The Successor Representation: Its Computational Logic and Neural Substrates. J Neurosci.

In this paper, Gershman presents and builds upon a reinforcement learning algorithm first proposed by Peter Dayan in 1993, called the successor representation, which solves some important problems that previous classes of reinforcement learning algorithms have had.  Recent research has supported the idea that humans and other animals use successor representations for learning and decision making under some conditions and has proposed that dopaminergic mechanisms could build successor representations in the brain. [Read More]