Targeted manipulation of activity in specific populations of neurons is important for investigating the neural circuit basis of behavior. Optogenetic approaches using light-sensitive microbial rhodopsins have permitted manipulations to reach a level of temporal precision that is enabling functional circuit dissection. As demand for more precise perturbations to serve specific experimental goals increases, a palette of opsins with diverse selectivity, kinetics, and spectral properties will be needed. Here, we introduce a novel approach of "topological engineering"-inversion of opsins in the plasma membrane-and demonstrate that it can produce variants with unique functional properties of interest for circuit neuroscience. In one striking example, inversion of a Channelrhodopsin variant converted it from a potent activator into a fast-acting inhibitor that operates as a cation pump. Our findings argue that membrane topology provides a useful orthogonal dimension of protein engineering that immediately permits as much as a doubling of the available toolkit.

Animals adapt their behavior in response to informative sensory cues using multiple brain circuits. The activity of midbrain dopaminergic neurons is thought to convey a critical teaching signal: reward-prediction error. Although reward-prediction error signals are thought to be essential to learning, little is known about the dynamic changes in the activity of midbrain dopaminergic neurons as animals learn about novel sensory cues and appetitive rewards. Here we describe a large dataset of cell-attached recordings of identified dopaminergic neurons as naive mice learned a novel cue-reward association. During learning midbrain dopaminergic neuron activity results from the summation of sensory cue-related and movement initiation-related response components. These components are both a function of reward expectation yet they are dissociable. Learning produces an increasingly precise coordination of action initiation following sensory cues that results in apparent reward-prediction error correlates. Our data thus provide new insights into the circuit mechanisms that underlie a critical computation in a highly conserved learning circuit.

In this Scientific Perspectives we first review the recent advances in our understanding of the functional architecture of basal ganglia circuits. Then we argue that these data can best be explained by a model in which basal ganglia act to control the gain of movement kinematics to shape performance based on prior experience, which we refer to as a history-dependent gain computation. Finally, we discuss how insights from the history-dependent gain model might translate from the bench to the bedside, primarily the implications for the design of adaptive deep brain stimulation. Thus, we explicate the key empirical and conceptual support for a normative, computational model with substantial explanatory power for the broad role of basal ganglia circuits in health and disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.