To track visual computations in the mouse SC, we recorded from hundreds of neurons simultaneously in all layers of the structure using multi-electrode silicon prongs (Du et al., 2011). The animal was head-fixed, awake, and moving on a running wheel, but not trained to perform any specific task, so we could best observe the autonomous visual functions of the SC. The recording electrodes were aimed at the dorso-medial portion of the SC, which processes stimuli in the upper visual field. Over the course of several hours, we presented a battery of visual displays, ranging from abstract stimuli like flickering checkerboards to those with ecological significance, like overhead looming disks.

In analyzing neuronal responses to these stimuli, we observed a systematic progression from the superficial layers that receive retinal input to the deep layers of the SC. To illustrate the dramatic change in how stimuli are represented, Figure 1 compares recordings from two sample neurons, one in the superficial SC and the other in the deep SC.

The superficial neuron responded well to many different kinds of displays, such as an expanding dark disk (the classic ‘looming’ stimulus), a contracting white disk, a moving disk, or a dimming disk. By contrast, the deep neuron was quite selective for the looming stimulus (Figure 1C). Second, the superficial neuron had a small and precisely circumscribed receptive field roughly 10° in diameter. It responded only when the stimulus invaded that region. By contrast, the deep neuron responded strongly to any looming stimulus presented over a wide region that encompasses much of the visual hemifield (Figure 1D). Third, the superficial neuron responded reliably to repeated presentation of the identical stimulus. By contrast, the deep neuron fired only on the first presentation and failed to respond entirely to the subsequent ones (Figure 1C, top row).

The three characteristics found in the deep SC neuron’s responses – selectivity for the looming feature, spatial invariance, and habituation to familiar stimuli – are all distinct from the signals transmitted by the retina. For example, an ‘approach-sensitive’ retinal ganglion cell (RGC) has been reported in the mouse retina (Münch et al., 2009), but later studies have found that it is actually the Off-transient alpha cell (Roska and Meister, 2014) that responds to many other Off-type stimuli in addition to the looming stimulus (Krieger et al., 2017). RGCs also have local receptive fields ranging up to 10° at most (Krieger et al., 2017), which can be readily mapped with white noise stimuli such as flickering checkerboards or bars (Zhang et al., 2012). Finally, although RGCs show complex adaptation properties, the timescale of adaptation is typically on the order of 0.1 -10 (Baccus and Meister, 2002; Wark et al., 2009), whereas the habituation we find in the deep SC lasts on the order of minutes. In the following sections, we elaborate on these response properties and how they may arise in the circuitry of the SC.

In an attempt to measure the visual receptive fields of all the recorded neurons, we applied a flickering checkerboard stimulus and then computed the spike-triggered average (STA) stimulus (Chichilnisky, 2001). This is a common procedure that works well for retinal ganglion cells and neurons in the early stages of visual cortex (Meister et al., 1994; Niell and Stryker, 2008). In the superficial SC, the STA analysis yielded linear receptive fields that resembled those of retinal ganglion cells (Figure 2A–B). They were sharply defined in space, with the smallest only ∼5° across. They frequently showed an antagonistic and delayed surround, and some displayed orientation- and direction-selectivity (Feinberg and Meister, 2015; Inayat et al., 2015). The great majority of these neurons (∼90%) were Off cells based on the shape of the STA. By contrast, neurons in the deep SC did not produce sustained responses to the flickering checkerboard (Figure 2A), and thus contained no structure in the STA (Figure 2B). Nevertheless, these same deep SC neurons did respond strongly to certain figural stimuli, like the expanding dark disk (Figure 2A,C–D).

Among the various figural stimuli we tested, many neurons showed some selective tuning (Figure 1C, Figure 2D, Figure 2—figure supplement 1). We focus here on the comparison of an expanding dark disk with a contracting white disk (Figure 2D). These two stimuli are closely related in terms of local features: both contain an advancing dark edge. But the ecological interpretations are quite different: one indicates an approaching dark object and the other a receding white object. Freely moving mice take an evasive action to an expanding dark disk, but are unimpressed by a contracting white disk (Yilmaz and Meister, 2013). Compared to superficial SC, neurons in the deep SC indeed became more selective for the expanding dark disk (Figure 2D). This can be seen as sifting what is likely the most behaviorally relevant signal in the upper visual field from other distracting stimuli.

Although superficial SC neurons often had sharp receptive fields just 5-10° in diameter, deep SC neurons generally responded to stimuli over a large part of the visual field. We probed this tendency with expanding dark disks presented at many different locations, as these were the most effective stimuli in the deep SC. With increasing depth in the SC, neurons showed larger receptive fields, growing by a factor of 6 in area or more (Figure 3A–B). Note that the resolution of the receptive field measurement with expanding dark disks is ∼15°, and as a result these receptive fields are larger than those measured by the flickering checkerboard (Figure 2B).

Despite this wide spatial range, deep SC neurons responded with a remarkably short latency to looming stimuli at any location (Figure 3A). By the time such a neuron starts firing, the expanding dark disk has only covered a few retinal ganglion cells. In contrast, for superficial neurons the latency varied depending on the location of the expanding disk stimulus and it often exceeded the latency of deep SC neurons. (Figure 3A). Figure 3C plots this variation in the latencies across the SC depth. One possible interpretation is that a widefield neuron in the deep SC pools over many local neurons in the superficial SC, such that it becomes sensitive with the same latency at every point in its receptive field. Indeed, such an interlaminar pathway has been demonstrated previously in slice preparations (Lee et al., 1997; Helms et al., 2004). We consider this possibility more thoroughly below.

In any case, it appears that certain widefield neurons in the deep SC have solved the problem of threat detection to a large degree: they signal the looming stimulus rapidly and sensitively without false alarms from stimuli that share some low-level features but not the behavioral significance.

Neurons in the superficial layers generally produced a spike burst of comparable firing rate with every repeat of the stimulus (Figure 1C). By contrast, some neurons in the deep layers responded with a sharp burst only to the first presentation; the response to all subsequent repeats was suppressed (Figure 4A). The degree of habituation to repeated stimuli was greater in the deeper SC compared to the superficial SC (Figure 4B).

The onset of this habituation is immediate and already affects the response ∼1 s later (Figure 1C, Figure 4A). The suppression then lasts for minutes: many deep SC neurons showed less than 50% recovery even after ∼120 s (Figure 4D). While we have not measured the exact time course of recovery, we found that the suppression was not permanent. In general, neurons recovered the full sensitivity to the first presentation when probed again about an hour later (Figure 4—figure supplement 1). Furthermore, the burst of spikes was not driven simply by a change in locomotor output or pupil size as a secondary consequence of the visual threat (Figure 4—figure supplement 2).

Remarkably, this habituation was strictly specific to the stimulus that caused the response. As reported above, widefield neurons in the deep SC can be triggered by looming disks at many different locations (Figure 1C, Figure 3A). Figure 4C shows the response of a single neuron to a looming stimulus whose location was chosen randomly on every trial. By comparing the sequence of responses at one location to that at another one can test whether the habituation transfers across space. As shown in the bottom left panel of Figure 4C,a stimulus at one location did not suppress the subsequent response of the same neuron to a stimulus at another location, even separated by as little as 15°. One interpretation is that the habituation takes place in local circuits spanning ∼15° in width before their output gets pooled by the widefield neuron.

Given that the memory for familiar stimuli can last 2 min or longer, we considered whether the hippocampus or the neocortex play a role in storing this information, perhaps by modulating the gain of collicular signals through the extensive projections from visual cortex (Zhao et al., 2014). Thus, we repeated the experiments in a mutant mouse that lacks all the dorsal forebrain, including the hippocampus and most of the neocortex (Kim et al., 2010; Figure 4—figure supplement 3). Intriguingly, the mutant also showed long-lasting suppression of repeated stimuli in deep neurons of the SC (Figure 4E), to a degree that matched the suppression seen in the normal mouse (Figure 4C bottom right and Figure 4D). This is consistent with a local mechanism for habituation within the SC.

The preceding analyses of single-neuron responses suggest that the neural population deep in the SC selectively represents those bits of information that may be of immediate relevance to defensive reactions, while other aspects of the visual display get discarded. To test this directly, we applied a linear decoder to the population vector from neurons in superficial and deep SC. From single stimulus trials, the decoder easily read out the precise location of a visual stimulus from the population in superficial SC, but much less so from neurons in deep SC (Figure 5, left). By contrast, the deep SC represented explicitly whether a stimulus appeared at a novel or a familiar location, whereas that information was barely available in the superficial SC (Figure 5, right). Of course a decoder with access to the entire history of responses could decode stimulus novelty also from the superficial SC. By contrast, in the deep SC that information is available on individual trials. In the next section, we explore how the information about stimulus history may be stored by the collicular circuit.

The microcircuitry of the SC is still poorly understood, at least compared to that of the retina. One can distinguish about 5 to 10 neuronal types based on morphology and gene expression (Byun et al., 2016; Gale and Murphy, 2014), but their synaptic connectivity is largely unknown. Furthermore, the SC interacts through long-range connections with other brain regions, notably the visual cortex (Seabrook et al., 2017). Nevertheless, it is useful to consider what circuit mechanisms may produce the observed visual responses of SC neurons. The functional evidence we have gathered here makes some potential explanations unlikely, and supports others as a guide in future studies of synaptic connectivity. Here, we focus on explaining three aspects of visual processing encountered in some deep SC neurons: the selectivity for looming stimuli, the invariance to spatial location, and the long-lasting stimulus-specific habituation. None of these phenomena occur in responses of retinal ganglion cells, and thus they must arise from post-retinal circuitry.

One circuit model that accounts for all the observed effects is shown in Figure 6A (‘the working model’). It starts with input signals from retinal ganglion cells. Those are combined to produce neurons selective for a local looming stimulus. The outputs of many such local looming detectors are pooled to produce neurons with widefield sensitivity and position invariance. Finally, the input synapses to those widefield neurons undergo a short-term synaptic depression that accounts for the stimulus-selective habituation.

To simulate the function of this circuit we modeled each of the neurons as a Linear-Nonlinear element (Chichilnisky, 2001), and the synapses according to a widely used formalism for short-term plasticity (Tsodyks et al., 1998). This model correctly recapitulates the preference for looming over other stimuli (Figure 6E); the position invariance; and the habituation to familiar stimuli (Figure 6F). It even accounts for detailed dynamics of the looming response in deep neurons, such as the short latency (Figure 3A) and the rapid quenching of the response caused by synaptic depression (Figure 1C, Figure 4A).

While a successful circuit model seems promising, one learns something useful only from comparing different explanations. Here, we consider several alternative microcircuits to account for the looming selectivity and the stimulus-selective habituation.

The working model (Figure 6A) builds on local looming-selective neurons. We encountered multiple cells in the superficial SC that match this profile: a local receptive field, looming selectivity, and little habituation (Figure 6—figure supplement 1). In the working model, this selectivity is achieved by combining signals from retinal ganglion cells (RGCs) with different dynamics: excitation from a fast and transient Off-cell forms the receptive field center, and inhibition from slow and sustained Off-cells forms the surround. Since RGCs are excitatory, the inhibition requires interneurons in the SC, and the slow dynamics of the surround may well result from filtering by those interneurons. In either case, the concentric organization of fast excitation and slow inhibition produces selectivity for looming over contracting white or moving or dimming stimuli (Figure 6E).

As an alternative explanation, could the looming selectivity already originate in RGCs? As we noted previously, the ‘approach-sensitive’ Off-cell that has been previously reported (Münch et al., 2009) is now known to correspond to the Off-transient alpha cell (Roska and Meister, 2014) which – while sensitive to looming stimuli – responds equally well to dimming and flashing spots (Krieger et al., 2017). Therefore, these RGCs do not qualify as the local looming detectors.

Another possibility is that looming selectivity results from a radial organization of direction-selective (DS) neurons, each of which reports a segment of the advancing dark edge (Figure 6B). Supposing those DS inputs come from the retina, the only candidates are the On-Off DS RGCs (Sanes and Masland, 2015), which would be equally sensitive to On edges. Thus, the looming detectors in the SC should respond to an expanding white disk as well, unlike what we observed (Figure 1C). If, on the other hand, the DS signals are generated de novo in the SC, one would expect to find such interneurons with all possible preferred directions. Instead, DS neurons in a given region of the superficial SC have a strong bias for just one or two preferred directions (de Malmazet et al., 2018). In summary, both of the considered alternative microcircuits for looming selectivity seem unlikely given the available evidence.

In the working model (Figure 6A), the stimulus-selective habituation is produced by activity-dependent depression of the synapses that convey the local looming signals to the widefield neuron. A plausible alternative mechanism would involve long-lasting inhibition of the looming detector from a neuron triggered by that same local stimulus (Figure 6C). This neuron would need to exhibit a sustained activity following a single stimulus. In our database of collicular recordings, we never encountered a neuron that matches this description. Another possibility is that local looming detectors – in addition to exciting the widefield neuron – also inhibit it via an interneuron (Figure 6D). Then the long-lasting habituation could be explained by the potentiation of the inhibitory synapse, rather than depression of the excitatory synapse. In that case, one might expect that repeated looming stimuli should produce a suppression of the ongoing baseline firing during later stimulus periods. We never observed such a suppression (Figure 4A). Instead the firing generally increased during stimulus intervals (rstim) compared to inter-stimulus intervals (risi) (for 15 deep SC neurons with baseline firing > 10 spikes/s, median rstim/risi, 25th-75th percentile range: 1.03-1.85).

In summary, several alternative explanations for the basic phenomena observed in deep SC neurons seem less likely than the working model that we propose, based on our database of extracellular recordings. We suggest that the key components of the working model in Figure 6A, namely the microcircuit for looming selectivity and the long-lasting synaptic depression, are fruitful targets for further investigation.

The superior colliculus (SC) presents an interesting interface between purely sensory representations and pre-motor signals. Our goal here was to follow systematically how the sensory inputs from the retina get digested and filtered in the SC. As a guiding problem we chose a robust visually-triggered behavior: the defensive reaction elicited by an overhead looming stimulus. By following visual responses of neurons from superficial to deep layers, we documented three aspects of the sifting process: (1) an increasing selectivity for the behaviorally relevant looming stimulus over other innocuous stimuli with similar low-level features (Figure 2); (2) an increasing invariance to other aspects of the visual display, such as the precise location of the threat stimulus (Figure 3); and (3) an increasing selectivity for novel over familiar stimuli (Figure 4). We considered how this filtering may be achieved by neural circuits and arrived at a plausible model of circuitry in the SC (Figure 6) that accounts for all three of the phenomena of visual sifting considered here. Moreover, several alternative circuit-level mechanisms were found to be inconsistent with the neural signals we encountered.

Some of the phenomena reported here have been described before in a wide range of species. A common theme is that neurons in deep SC respond over larger regions of the visual field, while retaining a preference for small stimulus features within that region (Cynader and Berman, 1972; Dräger and Hubel, 1975; Gordon, 1973; Humphrey, 1968; Ito et al., 2017). Also, the remarkably persistent habituation to repeated stimuli has been noted previously, even in the earliest recordings from optic tectum (Cynader and Berman, 1972; Dräger and Hubel, 1975; Horn and Hill, 1966b; Lettvin et al., 1961; Straschill and Hoffmann, 1969; Woods and Frost, 1977; Reches and Gutfreund, 2008). Another repeated observation is that the visual cortex appears dispensable for many aspects of visual processing in the SC (Horn and Hill, 1966a; Humphrey, 1968; Masland et al., 1971), although it does play a subtle modulatory role (Zhao et al., 2014). Looming stimuli are particularly effective for many neurons in the superficial SC (Zhao et al., 2014). Interestingly, the early literature missed this, perhaps because of the technical difficulty of generating an expanding dark disk with the commonly used hand-held slide projector (Dräger and Hubel, 1975). Our present report places these disjoint observations into a common context, namely the animal’s need to distill a specific signal of ecological value from the broad range of visual stimuli. We show that SC neurons are not only sensitive to looming stimuli but also become increasingly selective in deep layers, an essential requirement for an alarm system. Further we analyze the neural code at the population level throughout this brain region, which reveals the gradual progression of stimulus filtering. Finally, we consider how these aspects of neural representation relate to neural circuitry, and evaluate alternative hypotheses for such circuits. The results allow a broader consideration of how selectivity and invariance come about in brain processing, to be pursued further below.

The present study focused on stimuli presented in the upper visual field and recordings performed from the corresponding medial region of the SC. Arguably, the most behaviorally relevant event in the upper visual field is the impending arrival of a bigger animal, such as an aerial predator. The imminent threat that these events pose may account for the profuse responses to dark looming stimuli among SC neurons in this region (Figure 2; Zhao et al., 2014). Of course the threats must be distinguished from innocuous events, like the movement of overhead foliage, or the obscuring of the sky when the animal moves under shelter. The increased selectivity to the expanding dark disk in the deeper SC can account for that selectivity (Figure 2C, Figure 4B).

How should one interpret the profound habituation to repeated stimuli in this context? For one, the habituation does not interfere with the alarm response, since the animal must react to the first occurrence of a clear looming stimulus (Yilmaz and Meister, 2013). If the animal escapes or freezes, and the predator approaches a second time, this is likely in a different part of the visual field, and thus unaffected by the location-specific habituation. On the other hand, if the same stimulus recurs periodically in the same location, it is more likely caused by a leaf waving in the wind. Thus, the habituation can be seen as another processing strategy to reject innocuous events from the alarm pathway.

In the lower visual field the animal has different behavioral needs, such as picking out seeds against a cluttered background, following small moving prey (Hoy et al., 2016), perhaps identifying urine marks (Joesch and Meister, 2016), and tracking optic flow. Furthermore the connectivity between SC and other brain areas seems to differ in the upper and lower visual fields (Savage et al., 2017). Thus, one expects a corresponding difference in the rules by which visual stimuli are sifted there, a fertile area for future study.

One remarkable phenomenon in sensory processing is the emergence of neuronal responses that are both highly selective and broadly invariant. For example, certain ‘face cells’ in the primate visual cortex respond selectively to one person’s face regardless of the view angle, scale, or illumination (Freiwald and Tsao, 2010). How do these seemingly conflicting characteristics arise within sensory circuits? In the working model we propose here (Figure 6A) the answer is ‘first selectivity then invariance’. An AND operation across input neurons with different dynamics generates a local looming-selective neuron. These pattern detectors are distributed across the visual field. Then an OR operation pools across many local pattern detectors to produce the position-invariant response of the widefield neurons (Figure 7A).

This seems to be the scheme in other neural systems where the circuitry is understood. For example, in the auditory brain of the barn owl certain high-order neurons are selective for a particular interaural time delay, but invariant to the frequency of the sound (Konishi, 2003). These appear to arise from OR pooling over lower-order neurons that are selective for the same time delay but still tuned to different frequency bands. Those delay detectors in turn arise from an AND combination of signals derived from the two ears (Carr and Konishi, 1990). A similar processing scheme applies in the electrolocation circuits of weakly electric fish that exhibit a jamming avoidance response sensitive to frequency but invariant to many other parameters of the electric field (Heiligenberg, 1989).

However, this is not the only solution. In the case of face recognition, for example, it seems implausible that the brain should build separate pattern detectors for each face at each retinal location, and then pool over those to achieve invariance. An alternative scheme produces invariance first and then selectivity (Figure 7B). Here, there exists only a single pattern detector. But the inputs to this neuron are routed to ‘look at’ different spatial locations through a shifting circuit. The sudden appearance of any stimulus could engage these shifter circuits to route the corresponding low-level visual signals into the pattern detector (Olshausen et al., 1993; Ullman and Soloviev, 1999).

The observation of habituation and its specificity to location seems to greatly favor one of these schemes. Recall that habituation is seen prominently among neurons in the deep SC that are already highly pattern-selective. In the ‘selectivity first’ scheme, that places the gain modulation somewhere prior to the output of the pattern detector, which is the last spatially localized signal (Figure 7A). By contrast, the ‘invariance first’ scheme requires the gain modulation to occur in low-level visual neurons that are not yet pattern-selective (Figure 7B). This conflicts with our observations of neurons in the superficial SC that do not show location-specific habituation (Figure 4B). In summary, the robust observation of location-selective habituation in neurons of the deep SC favors a circuit model that develops selectivity before invariance.

Of course one can also envision intermediate solutions. For example, there is speculation that the visual cortex implements an alternation of AND and OR stages through a hierarchy of anatomical areas (DiCarlo et al., 2012; Riesenhuber and Poggio, 1999). Seeing that most vertebrate species do not have a neocortex, yet must solve the same problems of invariant pattern recognition, the SC seems like a promising arena for the study of high-level visual processing.

While it is tempting to suppose that the observed reduction of visual data is in fact performed within the SC, such anatomical localization is not a binding conclusion. In the extreme, the neurons of the deep SC, with their selectivity for fine spatio-temporal features and localized habituation, may simply reflect the output of a computation that occurs elsewhere. The SC interacts with many other anatomical structures (Basso and May, 2017; Savage et al., 2017), often in a reciprocal fashion. The most prominent such input, namely from the visual cortex, is likely not required for the essentials of visual sifting, based on our results with mutant mice (Figure 4E) and prior work with cortical silencing (Zhao et al., 2014) and ablation (Horn and Hill, 1966a; Humphrey, 1968). To contribute to sifting, the partner areas should retain a spatial resolution of the stimulus on the order of 10°. This constraint eliminates some small nuclei, but leaves several candidates in place, for example the thalamic area LP (Allen et al., 2016) and the parabigeminal nucleus. Given the position of the SC as a hub of brain pathways, it is an open question whether one can ultimately assign discrete computational functions to discrete anatomical areas.

On a finer level one may ask how the circuit models of Figure 6 map onto neuron types in the SC. About five cell types have been distinguished in the superficial SC of mammals based on morphology alone (Langer and Lund, 1974; May, 2006), and more recent studies have connected these types to visual responses and electrophysiological properties (Gale and Murphy, 2014). The most compelling by their visual appearance are the so-called widefield or bottlebrush neurons. These cells have a dendritic fan that extends towards the surface of the SC and spreads out laterally to cover a large area in the retinorecipient layers. Each dendrite terminates in a bottlebrush-shaped ending, and the overall morphology is startlingly similar across birds and mammals (Luksch et al., 1998; Major et al., 2000). The widefield neurons of mammals project to the pulvinar, and the axon forms multiple collaterals in the SC that could propagate the output to the deep layers (Basso and May, 2017; Major et al., 2000).

By virtue of their broad dendritic tree these widefield neurons offer themselves as the substrate for pooling across spatial locations, as in the working model of Figure 6A. Two further features recommend such an identification: First, the dendrites of widefield neurons generate spikes that propagate to the soma (Endo et al., 2008; Luksch et al., 2004). In this way, the neuron truly implements an OR operation across its inputs (Figure 7A): when any of its inputs fire, the output will fire. Second, experiments on chick tectum showed that each dendritic input undergoes a profound synaptic depression that lasts several seconds, but does not affect the function at another dendrite (Luksch et al., 2004). This could account for the location-specific habituation as in the model of Figure 6A. However, there is some question whether this synaptic depression also happens in the mouse (Gale and Murphy, 2016). Also we found a substantial increase of invariance below the anatomical stratum where the widefield neurons reside (Figure 3).

In summary, the visual response properties of deep SC cells differ dramatically from any signal that emerges from the retina, and it is tempting to associate this transformation with the bottlebrush neuron that is shaped unlike anything in the retina. Some caution is in order, of course. The diagram of Figure 6A should be viewed as a conceptual scheme rather than an explicit circuit with one-to-one corresponding real neurons. Perhaps the selectivity and invariance are accomplished in multiple stages, or with the contribution of other brain areas. Or the local looming detectors may be nonlinear dendrites, and ion channels with long-lasting inactivation (Ulbricht, 2005) may play the role of depressing synapses. The increasing availability of genetic handles for cell types in the SC (Byun et al., 2016; Gale and Murphy, 2014) should help in cracking some of these microcircuits.

We used C57BL/6 mice (RRID:IMSR_JAX:000664) aged 3-10 months (both males and females, Jackson Labs) for electrophysiological recordings. To prepare an animal for an experiment, we first implanted a metal headplate to the skull with a dental adhesive (3M Scotchbond) under anesthesia (2% isoflurane). After three days of recovery, the animal was habituated to being head-fixed on a circular treadmill for ∼30 min/day for 3 days. On the day of recording, the animal was again anesthetized and a craniotomy (< 1 mm diameter) was made over the SC (0.2-0.4 anterior to lambda, ∼0.5 mm lateral from midline). A small hole was made over the cerebellum to insert a silver reference wire. The craniotomy was then closed with a silicone elastomer (Kwik-Cast, WPI). After 6-8 hours of recovery, the animal was head-fixed and the craniotomy was exposed. A silicon neural probe was then lowered slowly into the brain (< 5µ m/s) and the depth from brain surface was recorded. The craniotomy was then covered with mineral oil to prevent drying of the exposed tissue. We waited a short period (15-30 min) for signals to stabilize before starting the recording. A typical recording session lasted 2-3 hours. All procedures were performed in accordance with institutional guidelines and approved by the Caltech IACUC.

The silicon neural probes were obtained from Sotiris Masmanidis (UCLA) (Du et al., 2011). For the majority of experiments, probe types 128A, 128AN, and 128DN were used. For data acquisition we used the RHD2000 128-channel amplifier board and the RHD2000 USB interface board (Intan). Auxiliary signals including the movement of the running wheel, timing of the stimulus, and timing of pupil video recording were collected concurrently with the neural signal. We used KiloSort (Pachitariu et al., 2016) for spike sorting of the data. The output of the automatic template-matching algorithm of KiloSort was visualized and manually curated on Phy (Rossant et al., 2016; Rossant, 2017).

To test if the long-lasting stimulus-specific habituation requires the neocortex and the hippocampus, we also recorded in mutant mice that developmentally lack these brain areas (Kim et al., 2010). These animals were bred by conditional knockout of exon 3 of Pals1 gene in cortical progenitor cells during embryonic development, achieved by crossing Pals1^flox/flox mice with LoxP sites inserted upstream and downstream of exon three with Emx1-Cre animals (Jackson Labs, Strain 005628) expressing Cre recombinase in the cortical progenitor cells. Conditional knockout of both copies of Pals1 due to Cre-mediated recombination during development resulted in Emx1-Cre:Pals1^flox/flox homozygous progeny used in this study (Figure 4—figure supplement 3).

The animal’s pupil diameter and locomotion on the circular treadmill were recorded along with the neural signals. The animals were not trained in any particular task and varied in their tendency to run on the treadmill. When looming stimuli were presented, the animals sometimes reacted by stopping (if the stimulus had arrived during a movement bout) or showing an increase in the pupil size (Figure 4—figure supplement 2C), but no characteristic behavioral output was consistently observed. However, we could rule out the possibility that the strong response of deep SC neurons to the first presentation of the looming stimulus is a simple consequence of motor output or change in pupil size, as they were usually not modulated by these factors in the absence of the looming stimulus (Figure 4—figure supplement 2). We also tracked the position of the pupil to monitor the eye movements. In many cases, the eyes were very stable, as demonstrated by the sharp (∼5°), circular receptive fields we recovered (Figure 2B) in the superficial SC by spike-triggered average analysis.

Prior to implanting into the brain, the tip of the silicon probe was covered with fluorescent lipophilic dye (DiD or DiI, Invitrogen). Immediately after recording, the animal was anesthetized and perfused with saline and 4% PFA. The brain was harvested and fixed with 4% PFA (Electron Microscopy Sciences) for 24-48 hours at 4°C, after which it was sectioned coronally at 100 µm thickness with a vibratome (Leica). The sections were then stained with anti-Calb1 antibody (Swant, CB-38a, 1:1000 dilution), which has been previously reported to label the superficial gray layers of the SC (Rousso et al., 2014). Following secondary antibody staining (AlexaFluor 488, donkey-anti-rabbit, 1:1000 dilution), sections were mounted with Vecta-Shield:DAPI and imaged using a confocal microscope (LSM800, Zeiss). From this we could estimate the location of the probe relative to SC layers (Figure 1—figure supplement 1). This histology-based method of localizing the probe relative the SC layers was complemented with current source density (CSD) analysis. First, the raw, broadband recording was low-pass filtered (150 Hz cutoff) to isolate the LFP band. Then the Laplacian of a column of spatially contiguous electrodes was computed and smoothed with a Gaussian kernel. This revealed a series of current sources and sinks in response to visual stimulation (Figure 1—figure supplement 1). By comparing this CSD analysis to the histological localization, we confirmed the results from Stitt et al. (2013) that the inflection point between the current source and sink marks the bottom of the superficial gray layer (SGS). We then defined the boundary between the superficial and deep layers as 100 µm below the inflection point (corresponding to 0 depth in Figure 2C–D, Figure 3B–C, and Figure 4B) to account for the thickness of the optic layer.

Visual stimuli were programmed using the Psychtoolbox (Brainard, 1997; Kleiner et al., 2007) package in MATLAB (Mathworks) and presented on a gamma-corrected monitor (IPS231, LG) at baseline luminance of ∼25 cd/m². The position of the monitor was slightly adjusted in each experiment such that the receptive fields of the neurons being recorded were located near the center of the monitor. Usually this was at ∼35° in elevation and ∼45° in azimuth (to the left) from the rostro-caudal axis of the animal. The monitor was located 15-20 cm from the animal and covered ∼120°of the horizontal field of view. The visual stimuli were synchronized to the neural recording by using a photodiode to send timing pulses from the monitor to the data acquisition board.

Before presenting the stimuli, we used a small flickering spot to map the part of the monitor that elicited strong neural responses (‘response zone’). Figural stimuli were then presented at these locations. The following is a description of each stimulus type during the stimulus period.

The progression of visual response properties with depth in the SC was discovered in early exploratory experiments. A subsequent round of recordings was performed to validate the initial observations. The present manuscript analyzes data from only these replication experiments. All analysis scripts were written in MATLAB R2016b (Mathworks) unless otherwise noted.

In the circuit of Figure 6A, we modeled each input neuron as a linear-nonlinear (LN) element. The neuron’s response was calculated as(1)r⁢(t)=N⁢(g⁢(t))where(2)g⁢(t)=s⁢(x,y,t)*k⁢(x,y,t)=∫x∫y∫t′=-∞ts⁢(x,y,t′)⁢k⁢(x,y,t-t′)⁢d⁢t′⁢d⁢y⁢d⁢xis the convolution of the stimulus s with the spatio-temporal receptive field k. The receptive field k⁢(x,y,t) was parametrized as(3)k⁢(x,y,t)=F⁢(x,y)⁢T⁢(t)(4)F⁢(x,y)=exp⁡(-x2+y22⁢σ2)(5)T⁢(t)=(tτ1)n1⁢exp⁡(-n1⁢(t/τ1-1))-b⁢(tτ2)n2⁢exp⁡(-n2⁢(t/τ2-1))

The nonlinear transformation was a half-wave rectifier:(6)N(g)=max(0,mg−θ)where θ is a threshold and m is a scaling factor. The firing rate of the local looming detector neuron (LD) was computed from the difference between the responses of the center and surround neurons:(7)rLD(t)=N(rc(t)−rs(t))and the response of the widefield neuron (WF) was computed from the various local detectors as(8)rWF(t)=∑iwirLD,i(t)where wi is the synaptic weight from local neuron i onto the widefield neuron. We modeled the habituation in the synapse between local detectors and the widefield neuron with a differential equation of three parameters for short-term synaptic depression and recovery:(9)ddtw=1−wτ−a(w−wmin)r(t)where τ is the time constant for synaptic recovery, a is a gain factor for depression, and wmin is a floor on synaptic strength. The simulation in Figure 6F used a=1 and wmin=0.

The temporal kernels used for the center and surround neurons feeding the local looming detector were taken from the measured receptive fields of mouse alpha retinal ganglion cells (Krieger et al., 2017). Table 1 lists the parameter values chosen. We arranged local looming detectors on a grid with 15° spacing between the centers of adjacent cells.