The subcortical visual system (SVS) is a collection of neuronal circuits in the midbrain, thalamus and hypothalamus that include the suprachiasmatic nucleus (SCN) of the hypothalamus, olivary pretectal nucleus (OPN), midbrain superior colliculus (SC) and thalamic lateral geniculate nucleus (LGN), among others. The LGN can be further divided into three anatomically and functionally distinct parts: the dorsolateral geniculate (DLG), ventrolateral geniculate (VLG) and the intergeniculate leaflet (IGL; Figure 1). These structures receive dense innervation both from the classical retinal ganglion cells (RGCs) and the intrinsically photosensitive RGCs (ipRGCs; Beier et al., 2021). The RGCs and ipRGCs integrate information from the rod and cone photoreceptors. In addition, ipRGCs express the photopigment melanopsin, which enables them to detect ambient light. These cells predominately communicate non-image-forming visual information to the brain for the pupillary light reflex, oculomotor functions and entrainment of circadian rhythms (Hattar et al., 2002; Schmidt et al., 2011). A subset of SVS neurons, such as the SCN and IGL, receives photic signals primarily from the ipRGCs (Morin and Allen, 2006; Schmidt et al., 2011).

Subcortical visual system neuronal centres also share another common characteristic – they all display oscillatory activities in a variety of frequency bands (from the infra-slow <0.01Hz to faster 30–70Hz oscillations; Figure 2). Interestingly, aspects of these rhythms are highly synchronised across the SVS due to their common extrinsic origin from the eyes, but synchronicity across these structures is also generated intrinsically. Thus, it is useful to categorise these oscillators according to the source of their rhythmicity. An ‘autonomous oscillator’ intrinsically generates rhythmic changes in its electrical and/or molecular clock activity. This ability is propelled at single cell levels which generate activity that are highly synchronised, or phase-locked. This in turn ensures a robust rhythm output emanating from the whole structure. A ‘semi-autonomous oscillator’ possesses all the necessary neurophysiological processes to express an intrinsic rhythm. However, it requires occasional rhythmic entrainment inputs to maintain its phase at the whole circuit level, an inherent property of the poor interconnectivity among its neurons. By contrast, a ‘slave oscillator’ does not exhibit intrinsic oscillatory activity but its rhythmicity can be completely driven by an autonomous or semi-autonomous oscillator (Guilding and Piggins, 2007).

In mechanics, the term ‘coupling’ describes a connection between two oscillating systems which affects the oscillatory pattern of both systems. In neuronal systems where signal multiplexing is common, the term coupling is used as an umbrella concept to overtly describe the intricate functional connections between neurons and/or neuronal ensembles. The neurophysiology of coupling is diverse and complex, representing the mechanistic nature of connectivity via synaptic and/or diffusive pathways that transmit the various temporal features of neuronal excitability. Coupling between neuronal centres organises their firing patterns according to a certain oscillatory frequency (Buzsáki, 2006; Buzsáki and Llinás, 2017). Such organisation may be achieved by: (1) the coupling of multiple oscillators firing in the same frequency band (synchronisation) or (2) the coupling of different frequency bands embedded in the activity of a single oscillator (multiplexing).

The SCN, canonically considered as the master circadian clock, is a component of the SVS and is localised in the anterior hypothalamus just above the optic chiasm (Takahashi, 2017; Hastings et al., 2019). It can be subdivided into two parts: the dorsomedial (dSCN) or shell and ventrolateral SCN (vSCN) or core. The SCN is directly innervated by the retina and mostly by ipRGCs (Hattar et al., 2006; Schmidt et al., 2011; Lokshin et al., 2015; Fernandez et al., 2016; Chew et al., 2017; Hastings et al., 2019).

The OPN is another target of dense innervation from ipRGCs, mainly segregated to its shell, rather than the core region. The OPN is localised in the pretectal complex and, through its connections with the Edinger-Westphal nucleus, controls pupil dilation and relaxation. Light potently excites OPN neurons leading to pupil constriction; thus, the OPN is mainly responsible for the pupillary light reflex (Young and Lund, 1998; Hattar et al., 2006).

The SC is a multi-layered structure localised in the midbrain. Its superficial layers are retinorecipient, innervated predominately by the classical RGCs, thus constituting a subcortical visual information-processing centre. Its function in rodents is to orient the animal towards the visual stimuli by combining visual sensory processing with motor coordination (May, 2006; Cang et al., 2018; Beier et al., 2021).

The DLG is a vision-forming part of the LGN due to its direct connections with the primary visual cortex via thalamo-cortical neurons. It is the most distinct part of the LGN complex both from developmental and functional perspectives (Sefton et al., 2015). The VLG makes reciprocal connection with the SC and the cerebellum; thus, it is believed to play a role in eye movements and other oculomotor functions (Graybiel, 1974; Born and Schmidt, 2008). The VLG is further subdivided into the directly retinorecipient lateral lamina (VLGl) and the brainstem information-processing medial division (VLGm; Harrington, 1997; Kolmac and Mitrofanis, 2000).

The IGL is a relatively small but important structure, intercalated between the DLG and VLG. It serves as a non-image-forming visual brain region that transmits both photic and non-photic entrainment signals to the circadian clock in the SCN. The IGL has also been implicated in the photic regulation of mood and the sleep/wake cycle (Harrington, 1997; Huang et al., 2019; Shi et al., 2019; Fernandez et al., 2020). The LGN complex constitutes a retinorecipient thalamic hub that integrates photic and non-photic cues for image- and non-image-forming visual purposes (Monavarfeshani et al., 2017; Figure 1).

Undoubtedly, the linking feature of the otherwise diversified functions of the SVS is the dense retinal inputs (Beier et al., 2021). Despite the segregation of RGCs innervation to these different brain centres, there are striking similarities in their spontaneous firing patterns. The retina itself is a robust autonomous oscillator (Freeman et al., 2008; Guido et al., 2010; McMahon et al., 2014). The firing pattern of the RGCs is reflected in the rhythmic release of neurotransmitters from RGC terminals in the brain. This results in rhythmic patterns of predominately excitatory postsynaptic potential (EPSP) activity in targeted SVS neurons which provides rhythmic excitation to this neuronal system.

The thalamus, including the LGN, is under a robust modulatory tone both from the cortical areas and arousing centres of the brainstem (Erisir et al., 1997; Harrington, 1997; Kolmac and Mitrofanis, 2000; Blasiak et al., 2006). Neuronal activities in the cortices and many brainstem nuclei exhibit profound changes that reflect general brain states during wakefulness and sleep (such as cortical activation and deactivation), which can be monitored by electrocorticogram (ECoG). In experimental setup, these general brain states can be studied in animals under urethane anaesthesia. During ‘urethane sleep,’ rodent ECoG exhibits rhythmic fluctuations which partially mimic the cyclically changing phases of sleep. During rapid eye movement (REM) or ‘cortical activation’, lower amplitude theta frequency waves are seen in the ECoG. In contrast, during non-rapid eye movement (NREM) or ‘cortical deactivation’ higher amplitude delta frequency waves occur in the ECoG (Balatoni and Détári, 2003; Clement et al., 2008; Pagliardini et al., 2013; Zhurakovskaya et al., 2016; Walczak and Błasiak, 2017).

Neuronal activity in the DLG and VLG, but not the IGL, is heavily modulated by these general brain states (Chrobok et al., 2018; Jeczmien-Lazur et al., 2019; Figure 3). It is most likely that brain state alterations that arise from sleep to wakefulness are the most profound changes to occur across a circadian cycle. It is therefore relevant to discuss how these general brain states modulate neurons exhibiting infra-slow and gamma oscillations. Recent evidence suggests that in the ‘cortical deactivation’ state, the extra-burst of the infra-slow oscillation is most often characterised by neuronal silencing (Chrobok et al., 2018). This means that while the brain is generally deactivated, the gamma rhythm is partially filtered at the level of the thalamus and may be transmitted to thalamic neuronal targets only during high activity in the intra-burst phase. The situation changes during the ‘cortical activation,’ which dramatically increases neuronal activity in the LGN (Chrobok et al., 2018). Thus, during the extra-burst, infra-slow oscillatory neurons remain active (although less active compared to intra-bursts; Figure 4). This implies that general brain state changes the gating properties of the thalamic neurons, and cortical activation promotes a constant flow of gamma oscillation [e.g., to the cortex (Saleem et al., 2017)], which amplitude is shaped by the infra-slow rhythm.

The SCN has traditionally been considered as the master circadian clock, which generates and coordinates daily rhythms in physiology across the brain and body (Takahashi, 2017; Hastings et al., 2019). With the discovery of multiple extra-SCN circadian timekeeping centres in the brain and peripheral tissues, it has now become clear that at least part of the rhythmic control of physiological processes must also develop at a tissue-specific local clock level (Guilding and Piggins, 2007; Albrecht, 2012; Myung et al., 2018; Begemann et al., 2020; Paul et al., 2020).

In support, recent investigations from our lab have reported circadian rhythmicity in core clock gene expression and neuronal activity in the SVS. This includes circadian oscillations in the IGL, VLG and SC (Chrobok et al., 2021b,c). Indeed, these rhythms in the electrical activity and clock gene expression were recorded under culture condition for several days in isolation from the SCN. This suggests that the circadian clock in the IGL, VLG and SC produces rhythms that are autonomous. In contrast, when isolated from SCN and in ex vivo conditions, the DLG shows circadian rhythmicity that quickly dampens and vanishes, suggesting that this brain locus functions as a slave oscillator. Additionally, our recent study reported a day/night difference in spontaneous firing rates in the OPN ex vivo (Chrobok et al., 2021a). Interestingly, the daily changes in neuronal activity are not in the same phase across the different SVS structures. For example, neuronal firing rate in the SCN, OPN and SC peaks during the day (light phase), whereas in the IGL and VLG, the firing peaks at night (dark phase; Belle et al., 2009; Hastings et al., 2018; Chrobok et al., 2021a,b,c; Figure 2). In the SCN, peak firing activity and Per1 gene expression coincide (Takahashi, 2017), whereas this is not the case for the IGL, SC and VLG (Figure 2). This may suggest a differential clock-controlled regulation of firing activity in these SVS structures which could be important for tissue-specific timekeeping across the SVS.

How circadian rhythms in the firing rate affect infra-slow and gamma oscillations remain an open question (Figure 4). A recent in vivo study has reported more frequent infra-slow oscillations in the SCN during the subjective day (when the SCN firing rate is high), than the subjective night (Tsuji et al., 2016). This may imply a circadian gating of infra-slow oscillations in this structure, similar to the gating effects of theta oscillations on gamma rhythms in the hippocampus (Jensen and Colgin, 2007). It is also plausible that this measured day-night change in SCN infra-slow oscillation frequency may be due to the inability for these investigators to reliably assess firing pattern in the SCN at night, when SCN neurons are hyperpolarised and firing at significantly reduced rate (Belle et al., 2009; Hastings et al., 2018). The possibility for circadian gating in the LGN would also be important. During the behaviourally active dark phase when photic information is sparse but critical for exploratory behaviour, the circadian drive of LGN neuronal activity peaks. This peaking in LGN neuronal excitability may serve to bolster information-carrying infra-slow and gamma oscillations across the SVS.

The SVS is a unique network of interconnected midbrain, thalamic and hypothalamic brain structures that operates in concert to process environmental light conditions. Its overall neuronal activity rhythm emerges from a combination of intrinsic cellular properties shaped by external oscillatory inputs, such as the electrical signal from the retina. Several oscillation frequencies are detected in the SVS areas, including gamma, infra-slow and circadian rhythms which may shape its operations. In addition, global brain states can also influence activity in the SVS. The plethora of frequency bands that emerge in the SVS, through the coupling of single SVS neurons, synchronise, interfere and multiplex with one another. Such complex interactions provide opportunities for slow rhythms to gate oscillations occurring at faster frequencies (Belle and Diekman, 2018; Figure 4). We hypothesise that gating created by multiscale coupling enables the SVS to manage important aspects of visual information flow in the brain at appropriate time. Unravelling the functional role for these distinct rhythms is challenging but critical if we are to understand the working brain.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

LC wrote the first version of the manuscript and prepared illustrations. MB and JM substantially edited and revised the manuscript. All authors contributed to the article and approved the submitted version.

This work is funded by a National Science Centre grant ‘Sonatina 2’ (2018/28/C/NZ4/00099) to LC, Biotechnology and Biological Sciences Research Council (BBSRC) Award to MDCB (BB/S01764X/1), and the Higher Education Sprout Project by the Taiwan Ministry of Education (DP2-109-21121-01-N-01, DP2-110-21121-01-N-01), the Taiwan Ministry of Science and Technology (110-2311-B-038-003, 110-2314-B-038-162, 110-2314-B-006-113, 109-2320-B-038-020, 109-2314-B-038-071, 109-2314-B-038-106-MY3) and Taipei Medical University (TMU107-AE1-B15, 107TMU-SHH-03) to JM.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.