Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical excitation of nearby cells can induce direct photocurrents in the postsynaptic cell. These photocurrent artifacts contaminate synaptic currents, making it difficult or impossible to probe connectivity for nearby cells. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are less variable than those of synaptic currents. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available as open source software.

Determining which features of the neural code drive behavior requires the ability to simultaneously read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two-photon calcium imaging and targeted photostimulation enable the activation of specific, functionally defined groups of neurons. However, these techniques are unable to test how patterns of activity across a population contribute to computation because of an inability to both read and write cell-specific firing rates. To overcome this challenge, we make two advances: first, we introduce a genetic line of mice for Cre-dependent co-expression of a calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we develop a method for read-out and write-in of precise population vectors of neural activity by calibrating the photostimulation to each cell. These advances offer a powerful and convenient platform for investigating the neural codes of computation and behavior.

The biophysical properties of existing optogenetic tools constrain the scale, speed, and fidelity of precise optogenetic control. Here, we use structure-guided mutagenesis to engineer opsins that exhibit very high potency while retaining fast kinetics. These new opsins enable large-scale, temporally and spatially precise control of population neural activity. We extensively benchmark these new opsins against existing optogenetic tools and provide a detailed biophysical characterization of a diverse family of opsins under two-photon illumination. This establishes a resource for matching the optimal opsin to the goals and constraints of patterned optogenetics experiments. Finally, by combining these new opsins with optimized procedures for holographic photostimulation, we demonstrate the simultaneous coactivation of several hundred spatially defined neurons with a single hologram and nearly double that number by temporally interleaving holograms at fast rates. These newly engineered opsins substantially extend the capabilities of patterned illumination optogenetic paradigms for addressing neural circuits and behavior.