# Computational optimization of two-photon holographic stimulation sites in vivo

## TL;DR

This paper introduces a new computational method to improve the precision of two-photon holographic optogenetics in stimulating specific neurons in the brain.

## Contribution

A novel real-time computational method using adaptive non-negative basis function regression to reduce off-target stimulation in optogenetics.

## Key findings

- The NBFR method can fit models for hundreds of neurons in seconds and optimize stimulation sites in milliseconds.
- The method was validated in simulations and in vivo experiments in mouse hippocampus.
- The approach effectively reduces off-target stimulation under realistic experimental conditions.

## Abstract

Objective. Determining the intricate structure and function of neural circuits requires the ability to precisely manipulate circuit activity. Two-photon holographic optogenetics has emerged as a powerful tool for achieving this via flexible excitation of user-defined neural ensembles. However, the precision of two-photon optogenetics has been constrained by off-target stimulation (OTS), an effect where proximal non-target neurons can be unintentionally activated due to imperfect spatial confinement of light onto target neurons. New approaches are therefore needed to resolve the OTS problem. Approach. Here, we introduce a real-time computational method for mitigating OTS that first empirically samples each neuron’s sensitivity to stimulation at proximal locations, and then optimizes stimulation sites using a fast, interpretable model based on adaptive non-negative basis function regression (NBFR). Main results. NBFR is highly scalable, completing model fitting for hundreds of neurons in just a few seconds and then optimizing stimulation sites in several hundred milliseconds per stimulus—fast enough for most closed-loop behavioral experiments. We characterize the performance of our approach in both simulations and in vivo experiments in mouse hippocampus, showing its efficacy under realistic experimental conditions. Significance. Our results thus establish NBFR-based photostimulus optimization as an important addition to an emerging computational toolkit for precise yet scalable holographic optogenetics.