1 Introduction

Optogenetics is now the principal experimental tool for manipulating neural activity in systems neuroscience due to its high temporal precision, non-invasive options, reversibility, and genetically and spatially restricted specificity (Boyden et al. 2005; O’Connor, Huber, and Svoboda 2009; Häusser 2014; Li et al. 2019; Emiliani et al. 2022). Optical access to the brain to stimulate opsins is predominantly achieved through chronically implanted fibers (e.g. Mayrhofer et al. 2019; Babl, Rummell, and Sigurdsson 2019; Lohse et al. 2021; Duan et al. 2021; Emiliani et al. 2022), or implanting a window over the cortical site (e.g. Schneider, Sundararajan, and Mooney 2018; Liu, Huberman, and Scanziani 2016). Implanted fibers allow for targeted light delivery in both head-fixed and freely moving behavioral paradigms and enable access to deep brain areas. However, the physical footprint and chronic nature of implants can restrict manipulation to a predetermined and limited number of sites.

In mice, the skull is thin enough (~250 µm; Ghanbari et al. 2019) to be remarkably transparent (Steinzeig, Molotkov, and Castrén 2017). This transcranial optical access allows for non-invasive, laser-scanning (random access) optogenetic photostimulation across the entire dorsal cortical surface. Various approaches exist to achieve this goal. An optical fiber can be mounted on a motorized micro-drive (Zatka-Haas et al. 2021), a digital micro-mirror device (DMD, Allen 2017) can project arbitrary illumination patterns (e.g. Chong et al. 2020), or a pair of galvanometric scan mirrors (galvos) can direct a focused laser beam to specified positions on the skull (Guo, Li, et al. 2014; Li et al. 2016; Heindorf, Arber, and Keller 2018; Inagaki et al. 2018; Pinto et al. 2019; Keller, Roth, and Scanziani 2020; Esmaeili et al. 2021; Voitov and Mrsic-Flogel 2022; Pinto, Tank, and Brody 2022; Coen et al. 2023; Gauld et al. 2025). A galvanometric approach to laser targeting offers the best utility-to-affordability trade-off: scanners are much faster than motorized drives, and because galvo mirrors effectively direct all light to the target site, the required laser power is much lower than a DMD. The high speed of the scanners nonetheless allows the experimenter to silence multiple locations near simultaneously.

While laser-scanning optogenetics approaches have been established in the field for several years, their use remains limited to a small number of technically sophisticated research groups. As these research groups have focused their publications on novel experimental results, it remains impractical for other groups to reproduce these methods without pre-existing expertise spanning optics, electrical engineering, and hardware control (Fig. S1). This represents a significant barrier, especially for newly established research groups. This is further compounded if the experimental aims differ from the original publication, or if specific parts are unavailable or custom-made. Therefore, despite its utility and potential, dissemination of laser-scanning photostimulation technology throughout the neuroscientific community has been limited.

Community-targeted versions of intricate experimental techniques facilitate widespread adoption: recent examples include comprehensive protocols and software tools for widefield calcium imaging (Couto et al. 2021) and two-photon all-optical interrogation (Russell et al. 2022). Furthermore, the widespread success of open-source hardware tools such as ‘Open-Ephys’ (Siegle et al. 2017) and the ‘Pulse-Pal’ pulse generator (Sanders and Kepecs 2014) demonstrate how accessible open-source versions of existing approaches can have a transformative effect on experimental neuroscience.

Here we present ‘Zapit’, an open-source galvo-based photostimulation system. Zapit integrates both hardware and software into a user-friendly experimental tool with functionality beyond that of any published system. It features a low-cost design based on commercially available components, with step-by-step build, usage, and troubleshooting instructions to promote accessibility. The modular hardware design allows users to modify the system to their experimental needs, including adaptations that support several stimulation wavelengths. The accompanying software facilitates semi-automated system calibration, flexible stimulus design and delivery, and is maintained by a community of developers and experimentalists. The system can interface with a range of programming languages for further integration into existing behavioural and experimental protocols. Through electrophysiological recordings, we confirm that Zapit’s flexible and accessible design matches the spatiotemporal resolution of existing laser-scanning solutions (Pinto, Tank, and Brody 2022, Guo, Li, et al. 2014, Li et al. 2019). Finally, we present proof-of-principle experiments demonstrating that Zapit can evoke robust effects across different sensory and motor systems in awake behaving mice, highlighting the utility and effectiveness of the system in revealing causal links between cortical areas and behavior.

2 Results

2.1 Operating principle

Zapit is a combined hardware and software solution for galvo-based photostimulation of the mouse dorsal cortex. We implemented a hardware design similar to Pinto et al. (2019) and Pinto, Tank, and Brody (2022), where a laser is directed into an XY galvo scanner, targeted to the animal via a dichroic mirror, and focused onto the sample with a scan lens. The scan lens doubles as an objective, imaging the sample and any excited auto-fluorescence onto a camera via a tube lens (Figure 1A). The camera provides a real-time image of the sample under the objective. This low-cost, accessible design is composed of commercially available parts (primarily Thorlabs) for easy set-up and usage (Figure 1B). The galvo waveforms are shaped to minimize mechanical noise from mirror movement, which should be consistent across trials. However, for use-cases where sound isolation is critical, we provide design-files for a custom enclosure for additional sound attenuation.

Figure 1.

The Zapit software comprises a MATLAB-based, Windows-run, user-friendly GUI to facilitate calibration and experimental integration. Calibration takes ~1 minute and involves two steps (Figure 1C): 1 mapping scanner positions to pixel-space in the camera image, and 2 mapping stereotaxic coordinates onto the image of the exposed skull. Once calibrated, the beam can be targeted via stereotaxic coordinates, and a simple API allows the user to integrate stimulation into an experimental paradigm. The software supports stimulation of multiple locations in a given experimental trial, with each location stimulated sequentially at a specified frequency (40 Hz in experiments described here). The laser intensity ramps down at trial end to limit rebound effects (Li et al. 2019). Detailed build instructions, operating guidelines and support are available via our GitHub repository (Figure 1D).

2.2 Hardware Design

Zapit is designed with commercially available components to ease construction and maximize affordability (Figure 2). The system is built around ThorLabs GVS002 scanners which direct the beam via an NI DAQ, and images are acquired using a USB-3 Basler acA1920-40um camera. The beam feeds into the galvo scan head via an adjustable fold-mirror. A second, fixed, fold-mirror placed before the camera keeps the system compact. To focus and image the beam we used an f=200 mm Plössl objective lens composed of two ThorLabs AC254-400-A (f=400 mm) achromats. A Plössl is a compound lens composed of two identical achromatic doublets arranged such that their positive (convex) surfaces are near touching. This arrangement has substantially reduced optical aberrations and distortions compared with a single achromatic doublet of equivalent focal length. We used an f=100 mm Plössl tube lens (2x ThorLabs AC254-200-A achromats), resulting in a magnification of 0.5x which comfortably fits the mouse brain onto the camera sensor.

Figure 2.

A variety of lasers, dichroics, and emission filters can be chosen to support diverse applications with different opsins. A complete parts list, with suggested alternatives, and assembly instructions, are available on GitHub. For a setup with a 473 nm laser (we used a Coherent Obis) targeting Channelrhodopsin-2 (ChR2), we recommend a ThorLabs MD498 dichroic to direct the light to the sample and allow reflected light to reach the camera. An MF525-39 emission filter in front of the camera allows visualization of autofluorescence from the target location without contamination from the excitation laser. We modelled the system in Zemax and calculated the theoretical laser spot as having a PSF with a FWHM of 70 µm. We measured the true PSF full width half max as 91 µm (Supplemental Fig. S2). Due to scattering the spot size within the brain will be much larger (~1 mm radius, (Li et al. 2019)).

The modular and open-source nature of Zapit means that exciting new configurations can be constructed, such as employing two lasers in one experiment to stimulate different opsins within one mouse (Supplemental Fig. S3). Future developments to Zapit’s control code will facilitate this use-case.

2.3 Software Design: calibration

The main Zapit GUI (Figure 3A-i) is launched by running start_zapit at the MATLAB command line. Using this GUI, calibration is a simple two-step procedure. First, we estimate the mapping from the voltage commands applied to the galvos, and the resulting laser location in the pixel-space of the camera image. This is achieved by scanning the beam over a grid of points and then conducting an affine transform between the intended and observed beam locations (Figure 3A-iii). Beam locations on the sample are automatically detected by the camera through image thresholding. The entire scanner calibration is therefore an automated procedure, initiated by a single button-click in the GUI. Following scanner calibration, the user can manually trigger ‘on the fly’ targeted photostimulation by clicking on locations the sample image in Point Mode (Figure 3A-iv).

Figure 3.

Following scanner calibration, the user calibrates the sample to brain atlas space by defining the position of the skull in the FoV using two landmarks, such as bregma and bregma +3 mm AP. These two landmarks are marked onto the skull during surgery (Supplemental Fig. S4). Whilst clicking these coordinates, a brain outline is dynamically positioned, scaled, and rotated, providing instant feedback on the calibration (Figure 4A-i-iii). This process assumes the skull is level under the objective, which should be considered during headplate implantation (Supplemental Fig. S4, and Methods). Once stimulus calibration is complete, photostimulation target locations can be specified in stereotaxic coordinates, relative to bregma (Figure 4A-iv).

Figure 4.

2.4 Software Design: defining stimulus locations

Stimulation target locations are defined in stereotaxic space and stored in a human-readable stimulus configuration file, which can be generated using the GUI (Figure 5A). This coordinate space is derived from the Allen Atlas zeroed to bregma (with bregma estimated at 5.4 mm AP in native space, as in Birman et al. 2023). Figure 5B shows an example of a finished stimulus set that includes unilateral, bilateral and multi-site stimulation conditions.

Figure 5.

The stimulus configuration GUI allows the user to define general stimulation parameters in addition to stimulus locations, including ‘Laser Power’, ‘Rampdown’ and ‘Stimulation Frequency’ (Figure 5C). Note that the ‘Laser Power’ setting defines the time-averaged power in mW at the sample surface used for stimulation. In other words, if two points are being stimulated in a single trial and requested power is 2 mW then the laser would deliver 4 mW at each point on the sample with a 50% duty cycle. A power meter receiving light from just one of those points would report an average power of 2 mW.

Stimulus sets can also be generated and modified by directly editing the configuration text file (YAML; Figure 5D). The configuration file can be loaded via the main Zapit GUI enabling photostimulation of the defined target areas (Figure 5E).

2.5 Running Experiments

Once the system is calibrated and a stimulus configuration file is loaded, Zapit’s MATLAB API can be used to integrate photostimulation into the user’s experimental protocol. Precisely timed stimulation events are delivered in a trial-based paradigm using the sendSamples function (Figure 6A-B), which operates via the NI Data Acquisition (DAQ) card connected to the control PC (Figure 6C). Analog outputs AO0 and AO1 drive the galvo mirrors, AO2 controls laser power, and AO3 drives the masking light. Trials can be optionally hardware-triggered via PFI0, and the system can be controlled remotely from MATLAB, Python, or Bonsai using Zapit’s TCP/IP protocol (Figure 6B). Photostimulation ends with a gradual ramp-down in power (by default over a 250 ms time period) (Figure 6D). This limits large rebounds in activity, which are common following photoinhibition cessation with light-sensitive opsins (Li et al. 2019).

Figure 6.

Zapit supports open-ended and duration-specific stimulation, with controllable stimulus features including stimulus duration, delay, and laser power. Usage of the API is documented on GitHub and examples are provided. Stimulation can be initiated immediately or deferred until a TTL trigger is received, which maximizes timing precision (0.5 ms onset latency and near zero jitter, Figure 6E). Stimuli can also be presented with a fixed delay following the TTL pulse, allowing for precise stimulus alignment with different temporal epochs of a behavioral task (Figure 6F). Zapit has the capacity to write a log file listing each stimulus presentation, so the order of events can be reconstructed post-hoc. This is particularly useful in scenarios where the user asked Zapit to produce randomly chosen stimuli. Finally, control trials are possible where the galvos move and the optional masking light is on, but the photostimulation laser is off. Although MATLAB is required to run the Zapit GUI and calibrate the system, experiments can be conducted using any desired programming language. Zapit can be controlled from either the local PC or a remote PC using TCP/IP communication. The Zapit zapit_tcp_bridge package contains Zapit clients for MATLAB, Python, and Bonsai. There is detailed documentation on the message protocol, allowing users to write clients in the language of their choice.

2.6 Beam Positioning Accuracy

In trials where ≥2 points are stimulated, the beam must be disabled (blanked) while traveling between locations to prevent off-target stimulation (Figure 7A-B). For simplicity, we chose a fixed motion time of approximately 0.5 ms between any pair of positions. This was long enough to significantly reduce sound generated by the scanners during experiments. The blanking period of the laser was manually tuned to correspond with the motion epoch (Figure 7C) and can be adjusted by the end user for different lasers and scanners. The extent of movement for the XY scanner will change depending on the number of points to be stimulated within a trial and the distance between individual points (Figure 7D).

Figure 7.

The number of sites per trial which can be effectively inactivated will depend on the available laser power. A reference heatmap (Figure 7E) shows an example of the estimated power per site relative to the number of sites.

We chose the Obis laser for its reliability, rise/fall times of µs, and linear power-voltage output. The Obis has sufficient power to maintain an effective average power across many (e.g. 20) sequentially targeted locations. We implemented blanking and power control directly using the Obis laser’s analog control input. This removes the added cost and complexity of an external modulator at the cost of a small non-linearity around 0 mW (Figure 7F). Even with a sinusoidal stimulation waveform, this discontinuity can create photoelectric artifacts with some electrodes, but does not have a significant physiological impact.

For comparison, we characterized reliability, stability and linear power-voltage output of other lasers (Supplemental Fig. S5), as researchers requiring a smaller number of laser spots per trial could use a cheaper option. When considering the maximal laser power required for an experiment, it is worth keeping in mind losses along the optical path, which can be high in certain circumstances (e.g. some scan mirrors have 20% to 30% losses at 488 nm).

2.7 Electrophysiological validation of optogenetic effects

To determine the spatial extent of neural modulation after light propagates through the skull, and relationship to laser power, we performed electrophysiological recordings during photostimulation (Figure 8A-B). We performed these validation experiments in awake VGAT-ChR2-EYFP mice which selectively express the excitatory opsin Channelrhodopsin-2 (ChR2) in inhibitory interneurons. Light stimulation therefore increases local cortical inhibition resulting in suppression of excitatory pyramidal neurons.

Figure 8.

To quantify the spatial spread of inhibition, we systematically varied the position of the laser beam across trials at distances ranging from 0 to 3 mm relative to the recording site. On laser-trials, putative excitatory neurons exhibited distance-dependent suppression of spiking that was time-locked to laser presentation (Figure 8C). Suppression was strongest when stimulation was delivered near the recording site and decreased progressively with increasing distance. However, we note that these silencing effects close to the recording site will be exacerbated by the necessity to remove skull to provide electrophysiological access to the brain. With 2 mW time-averaged laser power, we observed no change in firing rate ≥ 2 mm from the recording site. This spatial profile was consistent across cortical areas examined (Figure 8D), and comparable to previous reports of functional resolution using laser-scanning photostimulation (Li et al. 2019, Pinto et al. 2019). Optogenetic suppression of spiking also decreased as a function of cortical axial depth, with weaker suppression observed ≥ 1 - 1.5 mm from the cortical surface (Figure 8A,E). As expected, both the axial and lateral spread of photoinhibition increased with higher laser power (Figure 8E), with robust silencing effects measured 2 mm below the brain surface using higher power stimulation. Depending on the thickness of cortex at the stereotaxic location of stimulation, this means that deeper cortical and/or subcortical brain areas could be directly impacted with high-power stimulation conditions in transgenic mice with brain-wide opsin expression.

We also quantified the neural ‘rebound’ response following laser offset. Rebound refers to the transient increase in firing rate above baseline levels that can occur following cessation of optogenetic photocurrents. These effects can be mediated via intrinsic biophysical or synaptic properties and/or complex network effects as the circuit recovers from perturbation. Rebound responses are dependent on stimulation parameters, such as duration, laser attenuation duration (ramp-down), and laser power, as well as opsin and transgenic mouse line (Li et al. 2019). To assess post-laser changes in firing rate in our calibration experiment, we compared the relative change in firing rate across the pre- and post-laser periods (500 ms; Figure 8C Top). Consistent with previous reports, we observed power-dependent increases in rebound activity, with significant rebound effects at all laser powers above 1 mW (Figure 8F).

Combined, our data suggest limiting stimulation to 1-2 mW (time-averaged power) to minimize spatial spread of inhibition and rebound activity. If possible, users should perform calibration recordings under their own experimental conditions, which could deviate significantly from those shown above.

2.8 Behavioral verification of optogenetic effects

The ultimate purpose of the Zapit system is to facilitate in vivo optogenetic manipulation experiments that reveal causal relationships between brain activity and behavior. To demonstrate the efficacy of our system in achieving this goal, we performed behavioral validation experiments across multiple research groups and brain regions.

Delayed-response somatosensory discrimination

To establish whether Zapit could causally dissociate spatial and temporal neural processes, we trained mice on a delayed-response sensory discrimination task (Figure 9A; Gauld et al. 2024). Head-fixed mice received bilateral whisker stimulation (Stimulus epoch; 1 s), and reported the side receiving the stronger stimulus with directional licking following a short delay (Delay epoch; 1 s; Figure 9A-i). In expert mice, we causally tested 17 bilateral sites spanning the dorsal cortex (Figure 9A-ii). On 50% of trials, a single bilateral site was stimulated during either the Stimulus or Delay epoch. Mice performed with high accuracy on control trials without laser stimulation (P(Correct) = 0.89 + 0.08; mean ± std across 76 sessions from 4 mice). However, consistent with previous work (Guo, Li, et al. 2014), we found epoch- and site-specific photoinhibition effects on task performance (Figure 9A-iii). During the Stimulus epoch, only inhibition of barrel cortex and anterolateral motor cortex (ALM) significantly impaired correct choices. Barrel cortex is the area of primary somatosensory cortex (S1) that receives input from the whiskers, and ALM is a premotor cortical area involved in planning tongue movements, so inactivating these regions during the sensory processing epoch is expected to impair the animal’s ability to perform the task. During the delay epoch, we found particularly robust effects of ALM inactivation on choice accuracy and reaction time, consistent with this area being critical for motor planning and short-term memory maintenance (Guo, Li, et al. 2014, Li et al. 2016). We then performed unilateral inactivation experiments targeting ALM and observed robust ipsilateral choice biases, consistent with contralateral hemispheric motor-processing (Figure 9A-iv). As expected from our electrophysiological quantification, behavioral effects were power-dependent, saturating at ~2 mW (Figure 9A-v). Zapit was therefore able to confirm epoch-specific causal links between different cortical areas and distinct behavioral processes.

Figure 9.

Visual change detection

To demonstrate the versatility of Zapit across task designs, we performed a separate set of experiments in mice trained to report changes in the temporal frequency of a drifting visual stimulus (Figure 9B-i-iii; Orsolic et al. 2021). On control trials without laser stimulation, mice showed near 100% performance when the stimulus change was easy to detect. As expected for a visually-guided task, bilateral inactivation of primary visual cortex (V1) produced clear deficits in performance, with higher laser power resulting in stronger impairment (Figure 9B-iv). In contrast, inactivation of S1 did not significantly impact behavior (Figure 9B-v, consistent with the task having no somatosensory component). However, we did observe a small, but non-significant, decrease in psychometric performance during high power (4 mW) S1 photoinhibition (Figure 9B-v dark blue), which could be a result of unintentional off-target light ‘spillover’ impacting nearby higher-order visual areas. We therefore show that Zapit can also be used to generate robust and selective impairments in perceptual processing during visually-guided behavior.

Visual discrimination

To explore the effective spatial resolution of this inactivation approach with Zapit, we implemented a fine-scale causal mapping experiment in mice tasked to report the location of visual stimuli by turning a wheel (Figure 9C-i,ii; International Brain Laboratory et al. 2021; International Brain Laboratory et al. 2025). The stimulus-set comprised 52 bilateral sites positioned at 0.5 mm intervals covering a large network of frontal, motor, and somatosensory cortical areas. Photoinhibition of frontal motor cortex sites produced robust increases in reaction time (Figure 9C-iii left). In contrast, stimulation at other motor and somatosensory sites produced modest but significant decreases in reaction time. Our findings therefore demonstrate site-specific and opposing effects on the control of motor behavior across distributed cortical networks.

Together, these behavioral experiments demonstrate that Zapit enables precise and interpretable causal mapping of cortical contributions to behavior.

3 Discussion

Zapit is a complete and accessible solution for laser-scanning optogenetics. The components are commercially available, and the modular system is uniquely flexible to accommodate a diversity of experimental needs. The Github repository contains complete build instructions, parts lists, and visual aids: no prior expertise is required for implementation. The software is fully documented and will be continually maintained and updated by developers and experimentalists. Known software and hardware issues are actively raised and resolved through Zapit’s Github issues page.

Zapit comprises several technical developments that make it more versatile and user-friendly than any previously published system. These include detailed configurations for multiple lasers and opsins, user-friendly GUIs for automated stereotaxic calibration and stimulus design, and a cross-platform API for integration into diverse behavioral and experimental protocols across laboratories. Additionally, the modular design facilitates future developments, such as multiplexing two lasers in one setup.

Zapit produced robust behavioral effects across a variety of experimental protocols and brain regions with VGAT-ChR2 mice. We validated the efficacy of inactivation in this mouse strain with simultaneous electrophysiology recordings and demonstrate that the effective radius of inactivation depends on the laser power used, but was ~1 mm at powers for which we observed significant behavioral perturbations.

There are other effective approaches for rapid programmatic photostimulation in head-fixed behavioral tasks: those based on digital micro-mirror devices (DMDs – Chong et al. 2020) or spatial lights modulators (SLMs – Russell et al. 2022; Gauld et al. 2024). DMD-based solutions, such as the Mightex Polygon, provide a faster pattern-switching time but due to light loss, these systems require ~100 times more laser power than Zapit to cover the mouse dorsal cortex. SLM-based solutions are more light efficient, but devices with high resolution are very expensive and are slower than DMDs. Therefore, Zapit’s scanner-based approach provides the most cost-effective and compact solution for the majority of optogenetics experiments involving the dorsal cortex.

3.1 Practical considerations when using Zapit

Surgical procedures

To maximize targeting flexibility and accuracy, we recommend a head-fixation device that provides an unobstructed view of the entire dorsal surface of the skull, and marking structural reference points during surgery. We further recommend a thin layer of transparent glue (Zatka-Haas et al. 2021; Coen et al. 2023) or ‘bone’ cement (200-300 µm thick) to protect the skull, as thicker layers of cement (0.5-1 mm) may reduce stimulation efficacy. For general details of in vivo surgical and behavioral procedures in head-fixed mice we direct the reader to existing resources (e.g. Guo, Li, et al. 2014; Guo, Hires, et al. 2014).

Laser and power selection

Zapit is compatible with any laser source with fast analog power modulation. As a guideline, we recommend using 1-2 mW time-averaged light power when using VGAT-ChR2 mice. We consistently see strong behavioral effects at these powers, and spatiotemporal resolution comparable with many other studies. Regular power measurements at the sample are critical to ensure stability and reproducibility. At 2 mW time-averaged power, and with expected light loss of around 50% due to the skull and bone cement (Guo, Li, et al. 2014), heating of the neural tissue should be minimal (Stujenske, Spellman, and Gordon 2015). Nevertheless, post-hoc histological analysis is strongly recommended to confirm opsin expression and assess tissue integrity.

Targeting precision

With transgenic lines expressing opsin brain-wide (including VGAT-ChR2 mice), stimulation with high laser powers (e.g. 4 mW or above) may cause off-target behavioral effects due to light-scatter laterally and axially through cortex (Li et al. 2019; Babl, Rummell, and Sigurdsson 2019). Ideally, laser parameters including power, duration and ramp-down should be informed by electrophysiological recordings and/or modeling of light propagation through the brain (Yona et al. 2016). If limiting the impact of light dispersion is critical, opsins can be expressed via targeted viral injections. Under these conditions, expression can be restricted to highly localized circuits, genetically and anatomically defined subpopulations and sub-cellular compartments, and verified using post-hoc histology.

Sensory confounds

Fast movements of galvo-mirrors generate auditory cues that can influence behavior, and we therefore recommend moving the galvo-mirrors on all trials, including those where the laser power is 0 mW. Zapit behaves this way by default. The galvo drive waveforms are shaped to reduce noise emission, and the scanner unit can also be enclosed in sound-attenuating housing to further mitigate these effects. To accurately interpret optogenetic behavioral effects, it is also critical to mask the mouse’s ability to see the laser, whether through internal and/or external retinal stimulation (Danskin et al. 2015; Weiler et al. 2026). We recommend masking laser-emitted light with color-matched LEDs placed above the mouse on every trial, matched to the stimulation profile of the laser (e.g., 40 Hz), and using a well-lit setup. Extra caution should be taken when using longer wavelengths which easily propagate through brain tissue to the retina, and can be more difficult to mask (Danskin et al. 2015; Weiler et al. 2026). Sensory confounds can also be addressed through repeated experiments and analyses in control animals that do not express the opsin (Gauld et al. 2025).

Target region selection and interpretation

A major advantage of Zapit is the capacity to rapidly survey causal effects across the entire dorsal cortex. However, skull curvature and thickness should be considered, particularly in more lateral cortical regions. Differences in opsin expression due to tropisms in viral vectors or transgenic lines can also influence results. Users may need to perform further experiments to determine whether observed effects are a direct result of regional inactivation, or derived from downstream brain regions (Wolff and Ölveczky 2018; Allen 2017).

3.2 Good practice: measures to report when using Zapit

We encourage Zapit users to report a minimal set of standardized parameters to aid replication, comparability, and consistency across experiments and laboratories:

• Average light power per site

• Stimulation frequency and profile (e.g. 40 Hz sinusoid), and duty cycle

• Laser stimulation and ramp-down duration

• Specifications and duty cycle of any masking light

• Precise stimulation coordinates

3.3 In Closing

We hope Zapit is a tool that will grow and find a broad user base throughout the scientific community. Potential new users are welcome to contact us for assistance in building or setting up the system.

4 Methods

4.1 Recording and data acquisition for hardware characterization

Laser power output for all lasers was recorded using a Thorlabs Power Meter PM100D, and acquired with the associated Thorlabs Optical Power Meter Utility software. Samples for photostimulation reliability with the Obis 473 nm laser (Figure 6) were recorded at 100 kS/s using an oscilloscope (PicoScope). Samples for photostimulation reliability for all other lasers were recorded at 100 kS/s using a NI-DAQ USB-6453 and acquired with LabVIEW software. In both cases, laser output was recorded using a photodiode from a programmed 800 ms stimulation period, following a 200 ms rampdown. For users without a LabVIEW license who wish to acquire similar data we provide example code in Zapit (derived from our MATLAB DAQmx examples package) showing how to do so using NI DAQmx via MATLAB. Python NI DAQmx examples are also available.

4.2 Zapit configuration for experiments

In all experiments presented here we used a 473 nm, 75 mW Obis laser with a Thorlabs MD498 dichroic filter and an MF525-39 emission filter. The system was housed in a custom enclosure. ScannerMax Saturn 5 mirrors (available from Edmund Optics) were used to direct the beam. However, we recommend the ThorLabs version presented in this paper for most users, as it is more cost effective and easier to build. Zapit was controlled and data were acquired with an NI-DAQ USB-6363.

4.3 Mice

All experiments were performed under the UK Animals (Scientific Procedures) Act of 1986 (PPL: PD867676F) following local ethical approval by the Sainsbury Wellcome Centre Animal Welfare Ethical Review Body. We used VGAT-ChR2-YFP mice (Jackson Laboratories, USA) for all experiments.

4.4 Surgical procedures

Implantation of a clear skull cap (i.e., thin layer of transparent bone cement (C & B Superbond)) covering the dorsal surface of the brain together with a head plate placed over the cerebellum was carried out under 1.5% isoflurane in O2, together with pre and post surgical administration of Meloxicam (5 mg/kg). To ensure the skull is level under the objective, the skull should be aligned by checking that bregma and lambda depths are even (i.e., <0.05-0.1 mm difference), as well as ± 2mm lateral from midline is even (i.e., <0.05-0.1 mm difference). To allow calibration of the sample relative to stereotaxic space during Zapit experiments, two small dots should be made over bregma and +3 mm AP from bregma.

For electrophysiological validation of Zapit photoinhibition, craniotomies (~ 1.5 mm diameter) were made over secondary motor cortex (AP + 2.5 mm, ML ± 1.5 mm, from bregma) and visual cortex (AP −2.9 mm, ML 1.4 mm, from bregma). An additional cranial opening (~ 0.5 mm diameter) was drilled above the cerebellum to secure the ground wire to the skull. Then the craniotomy was covered with Dura-Gel (Cambridge NeuroTech), and a plastic protective cap was placed over the headplate to shield the exposed areas and maintain sterility between recording sessions.

4.5 Electrophysiological recordings

For electrophysiological validation experiments, neural activity was recorded using a 64-channel silicon probe (ASSY-236-H9, Cambridge NeuroTech) to minimize photoelectric artifacts. Signals were digitized at 30 kHz, amplified, and band-pass filtered (0.6–7500 Hz) using a 64-channel Intan Technologies headstage. Data were acquired, visualized, and stored using the Open Ephys acquisition board and GUI. During the recording, probes were lowered at 7 µm/s using programmed automatic micromanipulators (Sensapex), followed by a 10 minute settling period to minimize neural signal drift during recording. At the end of each trial, a serial TTL message encoding the current trial number was sent from behavioural control hardware to the acquisition system to synchronize the neural signal with the Zapit trigger signals.

Offline spike sorting was performed with Kilosort v4 (Pachitariu et al. 2024), followed by manual curation in Phy 2.0. Units were classified as putative neurons only if their spike waveforms exhibited a clear spatial decay across adjacent probe channels. Both single and multi units that showed low percentage of missing spikes, low refractory period violations, and minimal drift across time were included in the analysis. Units exhibiting substantial drift that led to a clear loss of spikes over time were excluded from further analysis. Additional details can be found in Gauld et al. 2025.

4.6 Delayed-response somatosensory discrimination task

The task design and experimental set-up were based on previous work (Gauld et al. 2024; Gauld et al. 2025). Briefly, mice were head-fixed in a perspex tube and air-puff stimuli (0 - 10 Hz; 20 ms pulse duration, 1 s sequence duration) were delivered to the left/right whiskers. Following a short delay period (1 s), mice reported their choice with directional licking to obtain water rewards (3 µl). The target response side was cued by the whisker side receiving the faster air-puff sequence, and randomized on trials with matched bilateral stimulation.

Following task learning, we surveyed 17 bilateral regions spanning the dorsal cortical surface (stereotaxic target coordinates in mm relative to bregma): (2.5 A ± 0.5 L), (2.5 A ± 1.75 L), (1.25 A ± 0.5 L), (1.25 A ± 1.75 L), (1.25 A ± 3 L), (0 A ± 0.5 L), (0 A ± 1.75 L), (0 A ± 3 L), (−1.45 A ± 0.5 L), (−1.25 A ± 1.75 L), (−1.25 A ± 3.5 L), (−2.7 A ± 0.5 L), (−2.5 A ± 1.75 L), (−2.5 A ± 3 L), (−2.5 A ± 4.25 L), (−3.75 A ± 1.75 L), and (−3.75 A ± 3 L). Laser stimulation (time-averaged power per site: 2 mW; duration: 800 ms + 200 ms linear rampdown) was delivered on 50% of trials, with target-site and epoch (Stimulus vs Delay) fully randomised. During the optogenetic mapping experiment, mice received unilateral whisker stimulation trials (10 Hz).

To examine putative epoch- and site-specific effects of laser stimulation on task performance, we compared Zapit trials for each bilateral target site with session-matched control trials using generalized linear mixed models (GLMMs). Specifically, we compared the predictive performance of the following two models shown below in Wilkinson-Rogers notation:

We performed 20-fold cross-validation to evaluate model predictive performance on out-of-sample test data. Mean log-likelihoods across matched folds were compared using a paired t-test, yielding a P-value for each cortical target site and each epoch. For analyses of reaction times (RTs), we compared Zapit and control trial RTs with a Wilcoxon rank-sum test. For performance and RT analyses, P-values were corrected for multiple comparisons across cortical locations using the Bonferroni–Holm method (α = 0.05, 17 cortical locations).

For choice-biasing experiments, we selectively targeted anterolateral motor cortex (“ALM”: 2.5 A ± 1.75 L) for unilateral photoinhibition. Laser stimulation (time-averaged power per site: 2 mW; duration: 2000 ms + 200 ms linear rampdown) was delivered on 33% of trials and randomized across hemispheres. During choice-biasing experiments, mice received nine sensory trial-types corresponding to a 3 × 3 combinatorial matrix of 0, 5, and 10 Hz left and right stimuli. Trials were delivered in randomized order.

4.7 Visual change detection task for proof of principle experiments using Zapit

The design of the behavioral task and stages of behavioral training were as previously described in Orsolic et al. 2021.

Mice were head-fixed and placed on a polystyrene wheel. Two monitors (21.5”, 1920 x 1080, 60 Hz) were placed on each side of the mouse at approximately 20 cm from the mouse head. The monitors were gamma corrected to 40 cd/m² of maximum luminance using custom MATLAB scripts utilizing PsychToolbox-3. The stimulus presentation was controlled by custom written software in MATLAB utilizing PsychToolbox-3. The visual stimulus was a sinusoidal grating with the spatial frequency of 0.04 cycles per degree resulting in 3 grating periods shown on a screen. Each trial began with a presentation of a gray texture covering both screens. After a randomized delay (at least 3 s plus a random sample from an exponential distribution with a mean of 0.5 s), the baseline stimulus appeared. The temporal frequency (TF) of the grating was drawn every 50 ms (3 monitor frames) from a log-normal distribution, such that log₂-transformed TF had the mean of 0 and standard deviation of 0.25 octaves and a geometric mean of 1 Hz. The direction of drift was randomized trial to trial between upward or downward drift. The sustained increase in TF, referred to in the text as change period, occurred after a randomized delay (3-15.5 s) from the start of baseline period and lasted for 2.15 s. Random 15% of trials were assigned as no-change trials and did not have a change period.

Mice were trained to report sustained increases in temporal frequency by licking the spout to trigger reward delivery (drop of soy milk). Licks that occurred outside of the change period are referred in the text as early licks. If mice moved on the running wheel (movement exceeding 2.5 mm in a 50 ms window) in either direction, the trial was aborted. If mice did not lick within 2.15 s from the change onset, the trial was considered a miss trial.

Optogenetic silencing commenced 0.25 s before visual stimulus onset using 40-Hz square-wave stimulation (50% duty cycle) delivered bilaterally throughout each trial. Following trial completion, laser power ramped down linearly over 0.25 s to minimise rebound excitation. To ensure mice could not distinguish laser from control trials based on visual cues, we combined two masking strategies. First, we illuminated the behavioural chamber with white LED strips throughout all sessions. Second, we delivered blue LED light from above the animal on every trial, matching the exact temporal profile of the optogenetic laser stimulation. This dual masking approach prevented mice from using visual detection of laser light to alter their behaviour.

We silenced primary somatosensory cortex (S1: ML=±3.2, AP=-0.8 mm) and primary visual cortex (V1: ML=±2.7, AP=-3.8 mm). Each area was silenced bilaterally using either 2 or 4 mW average power with each power level applied on 3.6% of trials per site. We also silenced five additional sites in these sessions not reported in this paper. 50% of trials served as no silencing controls where the laser was either turned off or targeted away from the skull.

Psychometric curves were calculated for each mouse by counting the number of hits relative to all trials where mice were presented with a change stimulus (i.e., did not early lick nor abort), across all sessions. Mean hit rates (performance) were calculated for each mouse (n = 5) per change size for either intact cortex (No area silenced) or for trials silencing each area with each light power. To ensure fair aggregation of trials across sessions, we only included sessions with performance above 80% hits on largest change (2 Hz) in cortex intact trials. We plotted the mean and 95% confidence intervals (i.e. standard error of the mean (s.e.m.) * 1.96) of hit rates across mice for each change size and each condition. We quantified differences between area silencing trials and intact cortex trials by performing paired t-tests (across mice) on the combined difference in performance for change sizes 1.25 and 1.5 Hz for intact cortex vs a specific area silenced (excluding 2 Hz to avoid potential statistical selection bias).

4.8 Visual discrimination task

Surgical procedures and behavioral training followed established protocols (International Brain Laboratory et al. 2021; International Brain Laboratory et al. 2025) with minor modifications. Mice were water-restricted and trained on the 2-AFC task using standardized training rigs. Mice were transitioned to the laser-scanning optogenetics (Zapit) rig upon reaching the “training 2b” performance criterion. The behavioral apparatus was adapted from the standard IBL electrophysiology rig design (International Brain Laboratory et al. 2021).

A grid of 52 bilateral stimulation sites was positioned at 0.5 mm intervals to cover secondary motor cortex, primary motor cortex, the upper limb region of somatosensory cortex as well as parts of anterior cingulate and prelimbic cortices.

Laser stimulation was controlled via TTL signals generated by modified IBL training software. On stimulation trials, TTL signals were sent at quiescent period onset and trial end (reward, error, or timeout). On each trial, there was a 50–70% probability that one bilateral site pair would be randomly selected for stimulation. Generally, the system was run with 2 mW laser power, with some exceptions for sessions in which 3 mW was used. To control for visual artifacts, a 473 nm LED masking light was positioned near the laser-scanning source and was activated on every trial coincident with the laser stimulation window, regardless of whether actual stimulation occurred.

Supplementary Figures

Figure S1.

Figure S2.

Figure S3.

Figure S4.

Figure S5.

Data availability

Data Availability: This is a tools and techniques manuscript. The code and CAD files for our opto-stim system are all available at this GitHub org: https://github.com/Zapit-Optostim/ Documentation is here: https://zapit.gitbook.io/user-guide/ Together the above provide everything that is necessary for the reader to recreate the system. The code and data to re-create the figures are here: https://github.com/Zapit-Optostim/overleaf-manuscript

Acknowledgements

We thank Andy Peters for providing us with a MATLAB script for displaying a top-down view of the Allen Atlas in stereotaxic coordinates, and inspiring us with the Neuropixels Trajectory Explorer (10.5281/zenodo.7043459). We thank Charu Reddy and Matteo Carandini for sharing resources to facilitate development, and Michael Krumin and Simon Weiler for valuable conversations. Dale Elgar from COSYS Ltd. did most of the custom enclosure design for additional acoustic attenuation. Graeme McPhillips at the SWC Electronics Core Facility provided advice on construction of the electronics enclosure. We thank Morio Hamada and Ivan Voitov for providing feedback on the manuscript.

This work was supported by Wellcome awards to T.D.M.F. (217211/Z/19/Z), M.L. (224121/Z/21/Z), O.M.G. (309104/Z/24/Z) and P.C. (225992/Z/22/Z). C.A.D. was supported by UKRI grant EP/Y008804/1. HF, NM, and AY were supported by The Wellcome International Brain Laboratory award (216324/Z/19/Z) and the Simons Culmination award (SFI-AN-NC-GB-IBL-00002672-07). The Advanced Microscopy Facility and individual labs were also supported by the Sainsbury Wellcome Centre’s core provided by Wellcome (219627/Z/19/Z) and the Gatsby Charitable Foundation (GAT3755).

Additional information

Author Contributions

Funding

Wellcome Trust (WT)

https://doi.org/10.35802/217211

• Thomas D Mrsic-Flogel

Wellcome Trust (WT)

https://doi.org/10.35802/224121

• Michael Lohse

Wellcome Trust (WT)

https://doi.org/10.35802/309104

• Oliver M Gauld

Wellcome Trust (WT)

https://doi.org/10.35802/225992

• Philip Coen

UK Research and Innovation (UKRI) (EP/Y008804/1)

• Chunyu A Duan

Wellcome Trust (WT) (216324/Z/19/Z)

• Sonja B Hofer

• Angela Y Yang

• Nathaniel Miska

Simons Foundation (SF) (SFI-AN-NC-GB-IBL-00002672-07)

• Nathaniel Miska

• Angela Y Yang

• Sonja B Hofer

Wellcome Trust (WT)

https://doi.org/10.35802/219627

• Peter Vincent

• Sonja B Hofer

• Oliver M Gauld

• Robert AA Campbell

• Quentin Pajot-Moric

• Chaofei Bao

• Chunyu A Duan

• Jingjie Li

• Nathaniel Miska

• Simon Townsend

• Maja T Quigley

• Michael Lohse

• Thomas D Mrsic-Flogel

• Nikolaos Zervogiannis

• Athena Akrami

• Jeffrey Erlich

• Angela Y Yang

Gatsby Charitable Foundation (GATSBY) (GAT3755)

• Oliver M Gauld

• Athena Akrami

• Maja T Quigley

• Chunyu A Duan

• Nikolaos Zervogiannis

• Michael Lohse

• Simon Townsend

• Jeffrey Erlich

• Jingjie Li

• Robert AA Campbell

• Thomas D Mrsic-Flogel

• Nathaniel Miska

• Quentin Pajot-Moric

• Peter Vincent

• Chaofei Bao

• Sonja B Hofer

• Angela Y Yang