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# Multisensory processes in birds: From single neurons to the influence of social interactions and sensory loss

Manon Rolland, Catherine del Negro, Nicolas Giret

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Manon Rolland, Catherine del Negro, Nicolas Giret. Multisensory processes in birds: From single neurons to the influence of social interactions and sensory loss. Neuroscience and Biobehavioral Reviews, 2022, 143, pp.104942. 10.1016/j.neubiorev.2022.104942. hal-04171727

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Multisensory processes in birds: from single neurons to the influence of social interactions and
sensory loss

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Manon Rolland, Catherine Del Negro, Nicolas Giret*

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Institut des Neurosciences Paris Saclay, UMR9197 - Université Paris Saclay

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151 route de la Rotonde

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91400 SACLAY

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France

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*Corresponding author : nicolas.giret@universite-paris-saclay.fr

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Abstract: World experiences involve multisensory stimulation that arises simultaneously from multiple
sources. Yet, we experience a coherent and unified world. Many studies have focused on how sensory
information from distinct modalities are integrated and showed that numerous behavioural and
cognitive benefits are provided by multisensory processes. Much work has been done with mammalian
models but research on avian species also expands our knowledge on multisensory processes. Avian
species exhibit a great diversity of behaviours and these species have provided evidence that
multisensory processes benefit by the learning that occurs in natural situations. Cross-modal
influences on the control of sensorimotor processes occur in circumstances of sensory loss. Also,
studies suggest pervasive multisensory influences throughout the avian brain. This review summarizes
research done on the imprinting behaviour of precocial bird species, on the ability of barn owls to
detect prey and on the vocal communication of songbirds.

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Keywords: Multisensory integration, cross-modality, imprinting, vocal communication, prey detection

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# 1. Introduction

We experience a unified external world from a vast amount of information processed through multiple sensory systems. This leads us to assume convergence and integration of information from these different modalities to create this coherent world. Within this framework, studies on humans and non-human animals have emphasized numerous perceptual and behavioural benefits from experiencing multiple sensory inputs (Stein et al., 2020). This includes a reduction in ambiguity, especially in noisy environments, lower sensory thresholds, enhancement of the speed and/or accuracy of stimulus detection, as well as facilitating the acquisition of higher cognitive functions and sensorimotor skills such as those involved in language and speech (Frassinetti et al., 2002; Frens et al., 1995; Lovelace et al., 2003; McDonald et al., 2000; Ross et al., 2007; Stein et al., 1989; Sumby and Pollack, 1954). The mechanism by which multisensory information is combined by neural circuits has been studied for many years. The number of identified multisensory anatomical circuits is continuously increasing and suggests that the perceptual benefit of multisensory integration is supported by functional interaction between sensory areas of different modalities from the early stages of sensory processing to more integrative levels (Cappe et al., 2009; Choi et al., 2018; Ghazanfar and Schroeder, 2006; Lohse et al., 2021a; Stein and Stanford, 2008a). Our understanding of neurophysiological mechanisms underlying multisensory processes has also increased (see Box 1 and Figure 1) and has revealed a set of empirical principles by which two sensory inputs interact to modify their neural responses (Stein and Stanford, 2008a; van Atteveldt et al., 2014).

Much of our knowledge of how multisensory processes are mediated, and at what level of hierarchy processing they occur, has been achieved by mammal studies. Surveys on bird species have also been useful in multisensory integration-related research. One of the main advantages of birds has been the opportunity to exploit their naturally occurring behaviour and the species-specific abilities they use in natural contexts. Bird groups exhibit a great diversity of behaviour, especially in their learning behaviour and/or specific abilities. As some of the abilities that bird groups can exhibit are not shared by mammalian species used as experimental animal models, birds enlarge the perspective on multisensory processing function. They also allow highlighting shared computations and neural
mechanisms with mammals, including humans. This report aims to summarize the current knowledge of multisensory processes based on behavioural, neuroanatomical and electrophysiological studies made on different avian species. Multisensory processes embrace various forms of complexity, from the “simple” cross-modal cue integration to top-down modulations by high-level cognitive functions (see box 1) (Meijer et al., 2019). We thus emphasized the synergistic effects of multisensory and learning processes in birds. We summarized the detection performance in barn owls because of their remarkable abilities to perform cue integration of auditory and visual information to detect a prey. We then described the neuronal mechanisms underlying cross-modal cue integration in the learning context of conspecific (parent) recognition by focusing on the imprinting behaviour exhibited by precocial avian species. Finally, we focus on the vocal communication in songbirds that has striking parallels with human speech. In songbirds, considered as auditory specialists, recent studies have opened up a new line of research. They have revealed the potential control of sensory inputs, other than auditory, on song behaviour, even in deaf songbirds. They have also extended our current knowledge on multisensory processes. They have provided evidence that cross-modal integration can serve not only sensory perception but also motor control of vocal production, making possible to finely adjust vocal acoustic features. Finally, we discuss how avian research can be informative on corresponding processes in the mammal brain.

## 2. Interconnected sensory pathways. Multisensory anatomical pathways in birds

The main limitation of using birds as a model organism system was once thought to be the large differences in brain organization between avian and mammalian species, including a lack of a neuroarchitecture comparable to that of the cerebral cortex. However, studies have now revealed many analogies or homologies in both brain structures and networks between birds and mammalian species. A cortex-like organization based on connectivity patterns has demonstrated that the avian brain organization is more similar to that of mammals than previously thought (Medina and Reiner, 2000a; Reiner et al., 2004; Stacho et al., 2020).

Multisensory processes in mammalian brains have been thought to result from the convergence of the different sensory streams in interconnected high-order cortical areas. The idea that multisensory
interaction is limited to high stages of brain processing has been challenged by studies demonstrating such interactions in the midbrain, particularly in superior colliculus, to cortical brain regions including primary sensory areas, classically considered as unisensory brain regions (Cappe et al., 2009; Stein and Stanford, 2008a).

In the avian brain, heteromodal connections at the different stages of sensory processing have also been described (Figure 2). Connections between the auditory and the visual pathways already occur in the midbrain: the auditory nucleus mesencephalic lateralis, pars dorsalis (MLd), also known as inferior colliculus and the optic tectum (OT), a midbrain visual area considered to be the avian homolog of the superior colliculus (Gutfreund et al., 2002; Hyde and Knudsen, 2000; Knudsen and Knudsen, 1983; Luksch et al., 2000). In many mammalian species, the generation and control of eye and head movement involves the superior colliculus, characterized by aligned maps of space for auditory, visual and tactile modalities (King and Hutchings, 1987; Sterbing et al., 2002; Wallace et al., 1996; Wallace and Stein, 1996). As in the superior colliculus of mammals, multisensory neurons that respond both to visual and auditory stimuli are common in the avian OT. In barn owls, auditory maps are located in the OT. This shows a remarkable topographic resolution that is aligned to the visual one (Wagner et al., 2013). Auditory or visual stimuli that originate from a single location in space activate the same set of neurons in the avian superior colliculus (Knudsen, 1982) suggesting that neurons in the TeO nucleus exhibit multisensory properties.

Importantly, multisensory convergence is not limited to the midbrain, similarly to mammals. Connections between the auditory and visual pathways have been identified in the avian pallium, where a cortex-like canonical circuitry exists. The analogue of the primary auditory cortex, so called Field L, connects with the homologue of the primary visual cortex, the visual part of the Wulst, (Medina and Reiner, 2000b; Stacho et al., 2020; Wild and Williams, 1999). At a higher stage, the non-sensory associative area, the nidopallium caudolaterale (NCL), considered as the avian analogue of the mammalian prefrontal cortex, receives inputs from both auditory and visual areas, as does its mammalian counterpart (Güntürkün, 2005; Kröner and Güntürkün, 1999). Also, tangential projections connect distant somatosensory, visual and auditory territories (Stacho et al., 2020). Thus, the
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connectivity pattern of the avian brain can mediate multisensory processing at different stages of the
brain organization.

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**3. Multisensory cue integration in an auditory specialist bird species: the barn owl**

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Cross-modal cue integration, which benefits stimulus detection or discrimination, is considered as a
form of multisensory processing which mostly relies on bottom-up neural mechanisms. In this section,
we focus on one bird species, the barn owl (*Tyto alba*), who uses cross-modal cue integration to detect
preys. Neuronal mechanisms underlying this behaviour are in described in the light of mechanisms
observed in mammals, including humans.

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**3.1 The prey detection behaviour**

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The barn owl (*Tyto alba*) provides an appropriate model system for investigating interactions between
visual and auditory information in the context of natural behaviour. This nocturnal bird species is
characterized by its high and precise efficiency to detect small prey by hearing (Konishi, 1973;
Hausmann *et al.*, 2008; Singheiser *et al.*, 2010). The barn owl is clearly a species specialized in
auditory processing. Yet, barn owls use cross-modal cue integration of auditory and visual signals
when light conditions are suitable (Knudsen et al., 1979; Konishi, 1973; Wagner et al., 2013). When
barn owls hear or see a potentially salient signal, their head turns in a quick saccadic movement to face
that signal. Barn owls can use auditory or visual cues alone to detect their prey (Hausmann et al.,
2008; Konishi, 1973; Singheiser et al., 2010). They are also able to exploit this signal combination
when it occurs simultaneously. In such a case, they use the fastest sensory signal, the auditory one, to
initiate their behaviour, *i.e.*, the saccadic movement and the most precise signal, the visual one, to
refine the location of the emitter. Consequently, this results in similar short response latency and high
spatial precision than when auditory and visual cues are presented alone, respectively (see more details
in a review Wagner et al., 2013; Whitchurch and Takahashi, 2006). Beyond this potential benefit from
an audio-visual stimulus, the audio-visual combination can impact the target selection. Barn owls can
adjust their visual search behaviour by shifting their gaze, according to the location of the sound
source. They select the visual target closest to the sound source, suggesting that auditory and visual

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information, when combined, are used for selecting the most relevant target rather than for simply localizing stimuli (Hazan et al., 2015).

## 3.2 Multisensory neuronal processes at play for detecting prey

In barn owls, cross-modal interactions influence spiking activity of TeO neurons (Knudsen and Brainard, 1991). Visual information occurring at a given location in the visual field enhances neuronal responses to sound originating from the same location while no such modulation is observed when visual stimuli originate elsewhere (Zahar et al., 2009). Also, bimodal responses of multisensory neurons exceed responses to unimodal stimuli (Zahar et al., 2009). However, this bimodal enhancement of neuronal responsiveness cannot be predicted by responses to unimodal stimuli since the number of spikes driven by the audio-visual compound is not higher than the sum of spikes driven by each unimodal stimulus (Zahar et al., 2009). In terms of the temporal pattern of spikes, bimodal compounds evoke responses that are better phase locked to stimuli presentation than responses based on the summation of unimodal responses. This suggests that multisensory tectal neurons exhibit a non-linear integration of auditory and visual information which is a property shared with neurons of the mammalian superior colliculus (Meredith and Stein, 1986). This might allow neurons to better detect events (Zahar et al., 2009). Cross-modal interactions also influence auditory maps in barn owls (Knudsen and Brainard, 1991). During development, manipulation of visual information, based on a prismatic effect, induces an experience-dependent shift in the auditory space map (Knudsen and Brainard, 1995, 1991). A similar effect has been reported in the mammalian superior colliculus: visually depriving ferrets during development leads to an abnormal auditory space map in this brain region (King, 1993; King and Carlile, 1993). This refinement of the auditory space map according to visual inputs in these two evolutionary distant animal species illustrates a crucial role of interactions between auditory and visual spatial information in the shaping of neurons. It also suggests a required matching of the representation of both auditory and visual space that depends on the experience. The adaptive alignment of the auditory space map sheds light on the importance of multisensory processes in coherent perception of the space.
The TeO, where many neurons are multisensory, is part of the tectofugal pathway, one of the two main avian visual pathways in addition to the thalamofugal pathway (Fig 1; Knudsen, 1982; Knudsen and Brainard, 1991; Reches and Gutfreund, 2009; Zahar et al., 2009). It sends its efferents to the thalamic nucleus rotundus which, in turn, projects to the telencephalic entopallium. This region is considered to be an analogue of the extrastriate cortex (Jarvis et al., 2005; Karten and Shimizu, 1989). The TeO receives inputs from the retinae but also from the external nucleus of the inferior colliculus, where an auditory space map has been described (Knudsen et al., 1987; Wagner et al., 1987). The external nucleus of the inferior colliculus projects to the main auditory nucleus of the thalamus of the ascending auditory system (the nucleus ovoïdalis). Given the hypothetical convergence of auditory and visual inputs, studies have examined multisensory properties of neurons along the tectofugal pathway. Neurons in both entopallium and nucleus rotundus respond to both auditory and visual stimuli (Reches and Gutfreund, 2009). In the entopallium, neurons show enhanced and earlier peaked responses to spatiotemporally congruent multisensory stimuli in comparison to responses to unimodal or incongruent stimuli. Responses of entopallium neurons to bimodal stimuli exceed the simple linear summation of unimodal responses, reflecting a non-linear integration of visual and auditory stimuli (Reches and Gutfreund, 2009). Therefore, the entopallium appears to be a brain region where multisensory properties exhibit some of the principles that characterize multisensory integration and that were reported in mammals (Stein and Stanford, 2008b; Wallace et al., 1996). Auditory responses in the entopallium were examined, after pharmacological inactivation of the TeO, to determine their origin (Reches and Gutfreund, 2009). A complete loss of auditory responses in the entopallium supports the hypothesis that auditory information ascends through the tectofugal pathway, which is a visual pathway in other species. In the context of this information *i.e.* bimodal responses in areas whose homolog regions in mammals are known to be visual, these areas might also be multisensory in other species but with a contribution of the auditory modality varying among species (Reches and Gutfreund, 2009). This view is consistent with the possibility that cortical brain areas are multisensory rather than sensory specific (Ghazanfar and Schroeder, 2006). In the chicken, a species not considered to be an auditory specialist, spatial auditory information is conveyed to the TeO. Therefore, the barn owl, as a species considered as specialized in auditory processing, led researchers to reconsider how
sensory information is processed throughout the brain (Reches and Gutfreund, 2009). Interestingly, the stimulation of a premotor area involved in the control of gaze direction induces changes in the auditory space map of the TeO (Winkowski and Knudsen, 2006), thus illustrating a top-down modulation that is reminiscent of the modulation of visual responses by stimulation of the frontal cortex in non-human primates (Moore and Armstrong, 2003).

In a framework for examining the importance of multimodalities on perception and detection of stimuli, another neural phenomenon is worth consideration. The stimulus-specific adaptation (SSA) phenomenon occurs in neurons that show a decrease in their responsiveness to frequent stimuli while rare stimuli reinstate strong responses. This neuronal adaptation has been proposed to allow deviance detection or surprise saliency by emphasizing unexpected stimuli relative to expected ones. The finding that neurons in the entopallium exhibit a SSA effect for visual stimuli suggests that SSA may be enhanced by pairing visual with auditory stimuli. One study addressed this issue and reported that rare bimodal stimuli evoke stronger responses than rare visual stimuli. However, no such difference was observed in responses to frequent stimuli (Reches et al., 2010). In addition, responses were enhanced when the visual stimulus was presented with an auditory stimulus that matched spatially but not when the bimodal stimuli were spatially incongruent. In a study based on a simple adaptation paradigm where the same stimulus was repeated several times, the first unexpected bimodal stimulus triggered a multisensory enhancement in the TeO while responses to subsequent stimuli did not show clear multisensory enhancement (Zahar et al., 2009). These findings provide evidence that the integration of visual and auditory information can benefit detection of unexpected or salient events and is achieved, as in mammals, by a network including midbrain and cortical areas, most probably in a bottom-up manner (Choi et al., 2018).

## 4. An example of cue integration learning: the imprinting behaviour

The bottom-up transfer of information in the sensory cortex can be modulated by top-down signals from higher-order areas involved in various cognitive functions. Studies in mammals, including humans (Robinson and Sloutsky, 2004), have shown that learning induces changes in the perception of sensory stimuli and that top-down signals contribute to modulate the neuronal responses of sensory
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cortex (Polley et al., 2006; Poort et al., 2015). In birds, the imprinting behaviour is a remarkable paradigm used for the study of neural mechanisms underlying learning processes. It has provided evidence that multisensory experience promotes learning, which might rely on the modulation of low-level areas by high-level areas through changes in neuronal responses to sensory stimuli.

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Imprinting has been described in precocial bird species such as chickens, ducks, and quail where neonate individuals are relatively mature and capable of locomotion in the immediate postnatal period (Lorenz, 1937). It involves the young bird approaching and following a stimulus, i.e. an animated object such as a moving red dot, or an individual such as a sibling (or mother, father), learning the characteristics of this stimulus, and consequently expressing a preference for that stimulus compared to a novel stimulus. Filial imprinting has been interpreted as being adaptive as it presumably increases survival of the young birds.

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Initial surveys mainly investigated the imprinting of young birds to a unimodal stimulus such as a visual stimulus (Bateson, 1978; Kovach and Hickox, 1971). The observation that mothers emit calls after egg hatching led researchers to expose neonate birds to either an audio-visual compound stimulus, i.e. a visual stimulus presented simultaneously with the maternal call or a visual stimulus alone (Bolhuis and Honey, 1994; Honey and Bolhuis, 1997). In domestic chicks, the presentation of the maternal call during training selectively enhanced subsequent chick preference for the visual training stimulus over a new one in comparison to conditions where the visual stimuli were presented alone (Kampen and Bolhuis, 1991; Van Kampen and Bolhuis, 1993). Reciprocally, simultaneous exposure to bimodal stimuli of the training compound affected the approach behaviour toward the training auditory stimulus (Kampen and Bolhuis, 1991). An enhanced preference for the visual stimulus was not observed when stimuli were presented sequentially or when the visual stimulus preceded the audio-visual compound (Honey and Bolhuis, 1997). This indicated the importance of the temporal congruency in the exposure to multisensory stimuli (Bolhuis, 1999). Altogether, these studies have provided evidence that learning in early life can benefit from multisensory information.

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A recent study that focused on recognition and generalization abilities also provided evidence that multisensory information benefits learning and perceptual abilities (Versace et al., 2017).

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Generalization through imprinting behaviour enables chicks to recognize familiar individuals in spite of changes in their appearance (e.g., mother hen and siblings seen from different angles). In this case, the natural imprinting stimulus was a single object such as a particular individual. Yet, an object can also consist in a temporal series of multiple elements characterized by their sequential organization, e.g., a series of multiple distinct sounds as, for example, words or sentences in human speech. Thus, the question was whether chicks are able to learn to recognize stimuli organized according to either an XX or XY pattern and to generalize to novel stimuli where X and Y are novel items. Multisensory information helps to identify and recognize objects or scenes (Shams and Seitz, 2008), enhancing the perceptual representation of sensory features of stimuli used. For example, a visual stimulus can enhance the representation of a temporally paired sound stimulus in the auditory cortex (Atilgan et al., 2018). This study demonstrated that multisensory information also helps to extract sequential patterns available for further generalization and categorization processes. The presentations of XX and XY patterns, where both X and Y consisted of audio-visual stimuli, was more effective than patterns in the visual or auditory modality for discriminating between novel stimuli according to their sequential pattern. This study provided evidence that multisensory information promotes the capacity to extract the sequential structure of stimuli (Versace et al., 2017).

This study raises the question of how multisensory association benefits learning. It is unclear whether, and to what extent, multimodal learning differs from unisensory learning. In imprinting studies, a significant behavioural approach to the training stimuli was observed in tests where presentations of only one sensory stimulus of the audiovisual compound, either the auditory stimulus or the visual stimulus, was presented (Kampen and Bolhuis, 1991; Van Kampen and Bolhuis, 1993). This suggested imprinting-induced changes in the representation of each unisensory information. Thus, it is unclear if these changes in the representation of each unisensory information stimulus are accompanied by the emergence of a representation of the multisensory compound that could form a unitary object. Some studies have addressed this issue by examining the neural mechanisms underlying imprinting. Recordings of neuronal responses were performed in birds trained with a visual stimulus associated with a taped maternal call (Brown and Horn, 1994; Horn et al., 2001; Nicol et al.,
266 1995). In chicks, the intermediate and medial mesopallium (IMM), a multisensory region that receives
267 afferents from all sensory systems and projects to motor areas (Brown and Horn, 1994; Horn, 1985;
268 Town and McCabe, 2011), is a critical area for the imprinting behaviour (McCabe et al., 1992, 1981).
269 Strong evidence implicates the IMM in recognition memory that underpins imprinting (Horn, 2004).
270 Neuronal recordings showed that imprinting using a bimodal stimulus affected both the number of
271 IMM neurons responsive to the visual or auditory component when presented alone, in comparison to
272 a novel stimulus of the same modality. This provided evidence for alterations in unisensory
273 representations of stimuli areas (Brown and Horn, 1994; Town and McCabe, 2011). Town and
274 McCabe (2011) also compared neuronal responses using different types of audiovisual stimuli,
275 including the training ones, novel or incongruent stimuli, the latter combining the training stimulus of
276 one sensory with a novel stimulus in the other modality. In this case, the training visual stimulus was
277 coupled with an auditory stimulus. Neuronal activity did not show any effect of training on responses
278 driven by the audiovisual training stimulus. Responses to the training compound stimulus were similar
279 to that of a novel one, although chicks were able to behaviourally discriminate between the imprinted
280 and novel audiovisual stimuli (Town and McCabe, 2011). Yet, results also indicated that the
281 incongruent audiovisual stimulus drove more responses from IMM neurons in imprinted than in naïve
282 chicks (Town and McCabe, 2011) suggesting that IMM could detect mismatching between the
283 auditory and the visual components of the imprinted stimuli. This suggests an encoding of the
284 multisensory training stimuli, but elsewhere in the avian brain than in IMM. It does not rule out the
285 possibility of a multisensory representation of the compound. These differences between imprinted
286 and naïve birds also illustrate that learning yields to changes in response properties of sensory neurons,
287 suggesting a local effect of learning and/or a top-down modulation exerted by IMM neurons on
288 sensory neurons.

289 In conclusion, precocial avian species through their imprinting behaviour have provided support for
290 the possibility that multimodal information promotes learning and also offered the opportunity to
291 study how multisensory inputs may lead to a unified representation of the multimodal object.
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## 5. Multisensory influences in a model system for studying vocal communication: the songbirds.

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Songbird research supports the idea that being an auditory specialist does not exclude the influence of other modalities, especially visual information. As an animal model system, songbirds have also provided unique evidence that cues from multisensory modalities can act in concert to promote the perceptual and sensorimotor skill learning required in vocal communication.

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Songbirds are a group of more than 4000 species with complex vocalization behaviour and high listening sensitivity. Birdsong is an acoustically complex communication signal that provides rich information about the singer and plays a crucial role in social interactions, especially in the contexts of mate attraction and territorial defence. Songbirds are one of the few non-human animal groups that learn their singing behaviour. During a critical period of at least several weeks, immatures need to hear an adult vocal model, called the tutor song, to learn to copy that song. They use auditory feedback to drive and maintain what they vocally produce. Eliminating or disrupting auditory feedback during song development results in abnormal songs (Iyengar and Bottjer, 2002; Konishi, 1965, 1964; Marler and Sherman, 1983). Deprivation of normal auditory feedback in adult songbirds also results in gradual changes in song structure (Brainard and Doupe, 2001, 2000; Horita et al., 2008; Lombardino and Nottebohm, 2000; Nordeen and Nordeen, 1992). Song behaviour has remarkable parallels to human speech given the strong dependence of hearing one's own vocal signals in both infants and adults (Doupe and Kuhl, 1999). However, human speech perception can be affected by visual information, as in the famous McGurk effect (Mcgurk and Macdonald, 1976) and ventriloquism effect (Slutsky and Recanzone, 2001): visual information provided by movements of the mouth and face strongly influences what is perceived (Kuhl, 2010, 2007). Also, human speech learning critically relies on auditory information perception although multisensory integration through information given by caregivers participates to learning progress (Mason et al., 2019). While literature from studies performed in mammalian models is rich on data providing evidence for multisensory processing in audiovisual perception and learning, knowledge remains sparse on how multimodal integration can shape motor production, i.e. lead to changes in acoustic features of vocalizations. Songbird vocal
behaviour offers the great opportunity to shed light on this role that remains to be explored. Songbird research has mostly focused on the role of auditory signals on vocal learning, perception and production. However, the influence of other sensory signals, including visual ones, has long been assumed (Hultsch et al., 1999; Todt et al., 1979).

## 5.1 Multisensory perception in songbirds

In songbirds, the auditory processing of vocal communicative signals involves a substantial number of brain regions including auditory areas as well as the interconnected neural networks specialized in song production and song learning, known as the song system (Fig. 3) in which neurons respond to song playbacks (e.g. Dave and Margoliash, 2000; Giret et al., 2014; Prather et al., 2008). Little is known about the neural pathways and mechanisms underlying multisensory processes in song behaviour. Some studies have reported anatomical evidence that nuclei of the song system receive inputs from the visual system (Paterson and Bottjer, 2017; Wild, 1994). Physiological studies showed that neurons in the song nucleus, at the departure of both the motor and the learning pathways, known as the HVC (proper name), respond to light flashes (Bischof and Engelage, 1985; Seki and Okanoya, 2006). Indirect evidence comes from investigations focused on the impact of social conditions at the neuronal level (Cousillas et al., 2006; George et al., 2011, 2012; Hessler and Doupe, 1999a; Menardy et al., 2014). Some of these investigations attempted to examine the respective contribution of visual and auditory components in neuronal responses (Avey et al., 2005; Cousillas et al., 2006). Social conditions may affect neuronal responsiveness in auditory areas (Menardy et al., 2014; Woolley and Doupe, 2008) including the auditory area considered as a homologue to the primary auditory cortex of mammals and consequently as a unisensory area, known as the Field L complex (Cousillas et al., 2006; George et al., 2012). In European starlings (*Sturnus vulgaris*), exposure to an audio-visual combination consisting of playback of conspecific songs and a picture of an individual evoked neuronal responses that differed from those evoked by the song playback alone (Cousillas et al., 2006; George et al., 2012). Therefore, even in a species known as a powerful model system for exploring the auditory processing of complex signals, visual stimuli may tune the primary auditory cortex. Furthermore, the modulation of auditory responses of single neurons in the Field L by visual stimuli
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depends on the degree of familiarity of the stimuli: seeing a familiar or an unfamiliar conspecific
yields to a suppression or an enhancement of auditory responses, respectively (George et al., 2011).

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Factors of social cognition may thus have an impact on the integration of multisensory signals and this
impact may occur in primary sensory areas.

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The avian primary auditory cortex, along with other sensory areas, projects to the NCL (Fig. 1),
considered to be analogous to the mammalian prefrontal cortex (Güntürkün, 2005). As such, the NCL
is a good candidate to investigate the neurophysiological mechanisms of multisensory integration at
the single cell scale. Electrophysiological recordings of NCL neurons were performed in carrion crows
(Corvus corone) previously trained to form arbitrary associations between a sound and a colour.

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During the learning, a set of NCL neurons was tuned to one or the other association between the
auditory and visual stimuli (Moll and Nieder, 2015), independently of the ordering of the stimuli (first
visual or auditory) (Moll and Nieder, 2017), such that the bird response could be predicted based on
the neuronal activity (Moll and Nieder, 2015). Corvids are songbirds who exhibit impressive cognitive
abilities related to interindividual competition and cooperation that depend on the processing of both
vocal and visual signals (Bugnyar, 2021). They live in complex fission-fusion societies in which
interindividual recognition is critical for group maintenance. This recognition involves discrimination
among conspecifics which can rely on multisensory signals, as in large-billed crows (Corvus
macrorhynchos) who use both the calls and the appearance of conspecifics to discriminate group
members from non-group members (Kondo et al., 2012). The integration of multisensory signals is
thus essential for social cognition, and the NCL may be a core area for this process (Brecht and
Nieder, 2020). The question remains open on how individual neurons, either in unisensory (Field L) or
high-order (NCL) areas acquire their multisensory properties. One possibility is that multisensory
properties actually arise in the NCL and contribute to modulate neuronal activity within sensory areas
in a top-down manner. Another hypothesis is that unisensory areas (e.g. Field L) acquire their
multisensory properties that eventually converge through bottom-up processes to higher-order areas,
such as in the NCL. Further experiments are required to assess more precisely these processes.

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Multisensory processes also play a role in female behaviour, particularly in their courtship behaviour that they display as receiver of songs (Amy et al., 2015). Receiver behaviour has been described as influenced by signals from different modalities in various avian species (Candolin, 2003; Mitoyen et al., 2019; Partan and Marler, 2005). In the non-songbird pigeon (*Columba livia*), an audio-videotape was more effective in triggering courtship displays from females than auditory or visual signals alone (Partan et al., 2005). In the brown-headed cowbird, the preference of females was assessed through their courtship displays to compound stimuli made from videos of a male paired with song playbacks.

Manipulation of visual or acoustic parameters of the compound stimulus indicated that changing the intensity of the video affected the attractiveness of the song playback and provided additional evidence for how visual signals influence song processing (Ronald et al., 2018, 2017). Female zebra finches elicit strong preference for the courtship songs compared to non-courtship songs, although the acoustic differences between these two song types are very subtle (Chen et al., 2017; Clayton, 1988; Miller, 1979; Woolley and Doupe, 2008). While singing for a female, male zebra finches also perform a stereotyped dance including head and body movements (Ullrich et al., 2016). Zebra finch songs may thus convey information on the social context in which they were produced (the male singing while alone or while socially interacting with a female). Interestingly, an fMRI study revealed that courtship and non-courtship songs induce different levels of activation of the caudocentral nidopallium (NCC), a sensory area outside of the classic avian auditory pathway, suggesting that this area plays a key role in the assessment of social, putatively multisensory, signals (Van Ruijssevelt et al., 2018). In canaries, females produce multimodal signals, a copulation solicitation display and calls, in response to conspecific songs (Amy et al., 2015). Lesions of the HVC nucleus in females change their behavioural responses to conspecific and heterospecific songs, suggesting that the HVC belongs to a neural network involved in the control of courtship cues (Del Negro et al., 1998). Anatomical surveys suggest that this neural network extend to downstream areas including premotor nuclei (RA and the respiratory premotor nucleus retroambigualis (RAm), Wild and Botelho, 2015). This overview suggests that feedforward mechanisms from sensory areas to premotor nuclei drive the multimodal courtship cues provided by females in response to male courtship songs.
399 **5.2 A multisensory approach of vocal learning**

400 Humans are not the only species in which vocal learning and perception are affected by social
401 interactions. Song learning is also shaped by social interactions that rely on sensory inputs from
402 several modalities. This suggests that information from multiple sources may be integrated or may
403 interact to influence the perceiver. Surveys focused on birdsong learning often compare the learning
404 performances of juveniles that experienced social interaction to those of juveniles that did not. For
405 example, auditory information through tape tutoring is as efficient as live tutoring in song sparrows
406 (*Melospiza melodia*) and swamp sparrows (*Melospiza georgiana*), but this is not true for other species
407 (Catchpole and Slater, 2003). For highly social birds like zebra finches (*Taeniopygia guttata*), song is
408 better learned from live tutors than from song playback (Eales, 1989; Slater, 1988). This raises the
409 question of whether visual and acoustic information provided by the adult tutor during interactions can
410 promote the song learning process. Such a question must then consider that other aspects can influence
411 vocal learning processes. The motivation to learn or the attention to the stimuli source (e.g. a talking
412 parent or a singing bird) are involved in vocal learning (Chen et al., 2016). In an attempt to control for
413 these factors, a focus on basic perceptual mechanisms indicated that, in nightingales (*Luscinia
414 megarhynchos* L.), juveniles can be tutored with a tape but their vocal learning performance is
415 improved when strobe flashes are tuned to be synchronous with song notes (Hultsch et al., 1999).
416 Other studies have addressed the issue by examining song acquisition in young birds that are allowed
417 either to visually and acoustically interact with a live tutor, or to other conditions that include
418 interacting only acoustically with a tutor or passively hearing the adult model (Chen et al., 2016;
419 Derégnaucourt et al., 2013; Varkevisser et al., 2022). Tutoring for as little as one day in zebra finches
420 is sufficient to highlight the benefit of multimodal information on song learning: interacting both
421 acoustically and visually with tutors made songs more similar to the tutor’s songs than interacting only
422 acoustically or passively hearing the adult models (Chen et al., 2016). Also, young zebra finches that
423 are either housed with, could see and hear, or only hear the same tutor showed that having access only
424 to auditory information delays the timing of, and reduces, the vocal learning (Varkevisser et al., 2022).
425 Song learning is consistently enhanced when the juvenile’s own songs are contingently paired with a
426 video playback of a female exhibiting a special behaviour, i.e. a wing stroke, that is considered as a
non-vocal feedback since it is commonly observed in response to attractive songs (Miller et al., 2008; Smith et al., 2000; West and King, 1988). Interestingly, young birds who received non contingent visual displays of a female feedback sang less accurate songs (Carouso-Peck and Goldstein, 2019). Another interesting approach is to use a stuffed model, or even better, robotic models that can be controlled by the experimenter (Araguas et al., 2022; Bolhuis et al., 1999). A robot can for example provide vocal responses (songs or calls) to the vocalization produced by the juvenile either in a contingent or non-contingent way, allowing to control for social interactions (Araguas et al., 2022). In humans, visual and acoustic information provided by adults during social interactions with infants enhanced acquisition of speech sounds (Goldstein et al., 2003; Kuhl, 2010). Babbling infants who received contingent feedback rapidly restructured their vocalizations (Goldstein and Schwade, 2008). Therefore, evidence that visual cues act in concert with auditory cues or influence the auditory processing to promote vocal learning in songbirds as well as in humans suggest that multimodal information plays a key role in vocal communication (Goldstein et al., 2003).

Birdsong learning has received attention from neuroscientists (e.g. Gobes et al., 2010; Okubo et al., 2015; Ölveczky et al., 2011; Shank and Margoliash, 2009). However, it still remains to investigate mechanisms underlying the impact of multisensory integration during development at the neuronal or network level. A network of brain areas has been proposed to mediate multimodal integration processes during song ontogeny, based on anatomical data (Paterson and Bottjer, 2017). Now, functional characterization remains to be carried out to better understand how multimodal integration maps onto the network.

## 5.3 A multisensory approach on the neuronal bases of birdsong production

Beside vocal perception and learning, vocal production in adults is another cognitive function in which multisensory processes likely play a role (Tremblay et al., 2003). In human speech, the monitoring of what is said does not exclusively rely on auditory information (Ito and Ostry, 2010; Lametti et al., 2012; Tremblay et al., 2003). Songbirds as an animal model system provide insights into this issue. In adult songbirds, social interactions based on multiple sensory modalities may shape singing behaviour. For example, male brown-headed cowbirds (*Molothrus ater*) rapidly adjust their song according to a
visual display, such as a wing stroke, provided by females (West and King, 1988). However, no study has investigated the extent to which socially-induced variations in song production depend on the visual component. In songbirds, the social environment is not the sole condition in which changes in vocal outputs may occur. Plastic changes in song features may be induced in a reinforcement learning paradigm. This paradigm commonly consists in providing auditory feedback, e.g. a loud white noise contingent to the fundamental frequency and the pitch of one song element (called a syllable) (Andalman and Fee, 2009; Canopoli et al., 2014; Tumer and Brainard, 2007; Warren et al., 2011). Singing birds alter the pitch of the target syllable allowing them to avoid the white noise delivery. Investigations based on this pitch-shifting learning paradigm have primarily investigated the role of auditory information in the monitoring of vocal signals. Recent studies have examined whether non-auditory feedback can substitute for the white noise. They provided the first evidence that a visual (a light-off or a light-on) as well as a tactile stimulus (applied to the scalp), respectively, can guide changes in the pitch value (McGregor et al., 2021; Zai et al., 2020). These results support the idea that the ability to carefully adjust one's own vocalizations, shared by humans and songbirds, can efficiently rely on information from several different modalities. These experiments have opened a new line of perspective supporting the idea that vocal production relies on multimodal processes.

Support for this hypothesis is provided by other studies. Multisensory processes are commonly considered to be susceptible to circumstances producing sensory loss. Zai and coll. (2020) showed that exposure of adult birds to visual stimuli in the pitch-shifting learning paradigm could drive vocal changes in hearing birds, as previously mentioned, but also in deafened birds. Male deafened zebra finches after bilateral cochlea removal are able to shift the pitch of one target syllable in the paradigm in which a light was switched off every time the pitch produced was lower (or higher) than a threshold value (Zai et al., 2020). These results demonstrate that visual signals in songbirds may exert an efficient control on vocal production when the auditory feedback is lacking. Importantly, birds with normal hearing and deafened birds exhibit differences in their behaviour adaptation. Deafened birds shifted the syllable pitch to increase the rate of visual stimuli delivery while hearing birds did the opposite. In both cases, somatosensory signals were available to control how to sing (Suthers et al.,
2002). Bird behaviour varied depending on the number of sensory modalities that conveyed information. Visual and somatosensory information was perceived in deafened birds while auditory information was also available to hearing birds. The magnitude of pitch changes induced by visual information also differed between hearing and deaf birds, being lower in hearing than in deafened birds (Zai et al., 2020). This lower magnitude of song changes in hearing bird could reflect a form of auditory dominance which minimizes the influence of visual signals. Similar sensory dominance was reported in an audio-visual fear conditioning paradigm in which the conditioned fear-response of rodents was weakened when a loud noise was simultaneously presented (Iurilli et al., 2012). In this experiment, the very high amplitude of the auditory stimulus provides more salient or reliable information than the visual stimulus. The auditory stimulus is thus prioritized over the visual stimulus, resulting in a segregation of sensory modalities in a bottom-up manner (Meijer et al., 2019). In the experiment performed in birds, hearing birds had access to both auditory and visual feedback while deafened birds only had access to visual feedback (Zai et al., 2020). The loss of auditory feedback may thus reveal the salience or the relevance of the visual feedback, that is constantly overshadowed by auditory feedback in hearing birds. Since hearing birds are able to adjust their song less efficiently than deafened birds, it suggests a dominance of one sensory (here auditory) modality in hearing birds and so a partial segregation of sensory modalities in a bottom-up manner. Given the fine motor control that the pitch shifting learning task requires, it might also be hypothesized that top-down processes from high order brain areas do occur to adjust the behavioural strategy (changing the song to get more or less visual stimuli). Further experiments will be necessary to understand how the respective influences of the different sensory modalities interact and control vocal production.

A specialized neural pathway is required for auditory-guided song learning during development and song maintenance in adults and is analogous to the basal ganglia-thalamo-cortical circuit of mammals. This pathway is involved in the drive of vocal plasticity induced either by visual or tactile stimuli in the pitch-shifting learning paradigm (McGregor et al., 2022; Zai et al., 2020). Lesioning Area X or LMAN, two nuclei of the avian basal ganglia-thalamo-cortical circuit, prevent the non-auditory-induced vocal plasticity (McGregor et al., 2022; Zai et al., 2020) demonstrating that this circuit is not
solely involved in the processing of auditory feedback in songbirds. Other evidence comes from
assessment of the impact of social context focusing on this pathway (Kao and Brainard, 2006). In
zebra finches, adult males produce a highly stereotyped song with slight variations between renditions.
In the presence of a female, males produce courtship songs that becomes even more stereotyped
(Sossinka and Bohner, 1980; Dunn & Zann, 1996; Hessler & Doupe, 1999a; Kao et al, 2005). The
basal ganglia-thalamo-cortical pathway is strongly affected by social cues (Hessler and Doupe, 1999a;
Jarvis et al., 1998; Kao et al., 2008). Neural recordings showed that the pattern of singing-related
neural activity in two regions, the Area X and the LMAN, is dependent on whether the bird is singing
in isolation or to a female (Hessler and Doupe, 1999a, 1999b; Kao et al., 2008). Therefore, as
information from different modalities may guide learning-induced changes in song structure, the avian
basal ganglia-thalamo-cortical pathway could be viewed as mediating vocal changes from different
sensory information and not exclusively from auditory information. Multisensory neurons in the basal
ganglia of mammals exhibit the ability to integrate multisensory information (Nagy et al., 2006; Reig
and Silberberg, 2014). Multisensory integration may help in the effective processing of sensory events
and the changes in the environment during motor actions controlled by the basal ganglia. Interestingly,
an early survey reported a positive correlation between the volume of the area X and of the visual
nucleus rotundus and the rate of courtship songs, but not with the rate of non-courtship songs in male
brown-headed cowbirds. This result thus highlights that the female-directed singing is a multimodal
courtship behaviour not only recruiting a song-related nucleus (Area X), but also a visual area (nucleus
rotundus).

In summary, research findings highlight the powerful influence of multisensory conditions on
behaviour, in juveniles as well as in adult males and females. Birdsong, as human speech, plays a
critical role in the social life of individuals. The findings demonstrate that social interactions, through
multisensory signals, also strongly influence vocal behaviour. Neurophysiological studies have
identified many brain areas where signals from multiple sensory sources may potentially converge.
Yet, it is unclear how the neural mechanisms by which information from different sensory modalities
interact or are integrated to influence songbird behaviour. There is currently great interest in the basal-
ganglia forebrain circuits for deciphering neural mechanisms underlying the learning and generation of
song behaviour. Recent evidence indicates that these circuits are required for non-auditory information
to drive changes in the song features of adult songbirds (McGregor et al., 2022; Zai et al., 2020).
Future research will help resolve the neural mechanisms by which non-auditory information exerts its
influence.

## Conclusion and Perspectives

This review focused on various avian taxa, behaviours and brain networks and highlighted fields of
research that deserve more investigations to better understand the processing and control of
multisensory signals (Figure 4). Our main goal was to highlight the current state of the art on
multisensory processing in birds. Recent reviews have been focused on the neuronal substrates
underlying multisensory processes but they were essentially restricted to research in mammalian
models (Meijer et al., 2019; Stein et al., 2020). Yet, a clear strength of the research on multisensory
processing in birds is, to our view, related to the type of behaviours being investigated. Prey detection
as well as imprinting or vocal communication are behaviours observed in natural conditions. Although
controlling all factors is a tremendous task when studying natural behaviours, they shed light on
specific processes that could not be revealed by simpler behavioural paradigms (Khastkhodaei et al.,
2016; Meijer et al., 2019). The research findings for example support the idea that facing a given
object through more than one sensory modality, as is often the case in the natural world, makes
cognitive abilities, including the evaluation of the object salience as well as acquisition of
sensorimotor skills, more effective.

This review aimed at highlighting the relevance of studying multisensory processing in another group
of animals than mammals. To this end, we have attempted to present multisensory processes in the
light of what is known from mammalian models, when data were available. This comparative
approach has allowed us to establish that some of the neuronal mechanisms identified in mammalian
models are also implemented in birds, thus arguing for evolutionary conserved strategies within brain
networks to achieve multisensory integration. Several examples indeed argue for bottom-up or top-
down modulation various forms of multisensory processing. In birds as well as in mammalian species,
multisensory processes affect numerous brain areas and pathways, ranging from the midbrain to primary sensory or high-order sensory cortical areas including brain regions of the basal ganglia-thalamo-cortical pathways. The availability of the range of techniques (e.g. large scale electrophysiological recordings, optogenetic methods) used in birds now allows to investigate not only neuronal dynamics but also to assess causal relationships between neuronal activity and behaviours that may depend on multisensory integration (Guitchounts et al., 2013; Roberts et al., 2012).

Beyond similarities between birds and mammals highlighted in this review, birds have allowed to raise original questions that mammalian species used as animal models left unresolved. The importance of multisensory signals in the fine motor control required by birdsong production to adjust acoustic features of vocalizations (McGregor et al., 2022; Zai et al., 2020), or in the case of a sensory loss, has opened a new line of research. This new field of research leads to assume that multisensory information may guide changes in vocal production in species that learn to vocalize such as humans.
Studies of multisensory processing in birds should provide new insights on mechanisms underlying speech acquisition and production in the human brain.

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<figure><img src="image_3.png" /></figure>

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<caption>Figure 1: Expected neuronal responses to unisensory and multisensory stimuli</caption>

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In response to a multisensory stimulus (modalities a – green diamond - and b – black circle), neuronal responses may result from linear properties (summation of the firing rate, FR) or non-linear properties (FR decrease, FR increase, and/or different temporal spike patterns).

1068
<figure><img src="image_4.png" /><figcaption>Figure 2: Interconnections between visual and auditory pathways in the bird brain.</figcaption></figure>

Birds have one auditory pathway (green) and two main visual pathways: the tectofugal (left, brown) and the thalamofugal (middle, blue) pathways. Interconnections between visual and auditory areas are reported in the midbrain (optic tectum – MLd, red arrows). In the pallium, direct connections have been identified between visual and auditory centres (Wulst – Field L, red arrows). The Wulst is considered as analogous to the mammalian primary visual cortex (V1). Visual and auditory information converge in the NCL (nidopallium caudolaterale, considered as analogous to the mammalian prefrontal cortex). In songbirds, there are indirect projections to the song system (dashed line) originating from visual relay (auditory areas projections to the song system are not shown). MLd: nucleus mesencephalic lateralis, pars dorsalis (indirect connections between the cochlear nucleus and MLd are not shown); (r)UVA: (rostral) nucleus Uvaeformis. For more details on the Wulst and (r)UVA circuits, see (Stacho et al., 2020; Wild and Gaede, 2016, respectively).
<figure><img src="image_5.png" /><figcaption>**Figure 3: The auditory pathway and the songbird song system**</figcaption></figure>

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The song system includes the song motor pathway (HVC: proper name; RA: Robust nucleus of the arcopallium, analogous to the human laryngeal motor cortex) and the anterior forebrain pathway (IMAN: lateral magnocellular nucleus of the anterior nidopallium; Area X, analogous to mammalian basal ganglia; DLM: dorsolateral nucleus of the anterior thalamus). It receives inputs from the auditory pathway (green), visual areas (red line, see Figure 1 for more details) and the ventral tegmental area (not shown). CM: Caudal mesopallium; NCM: Nucleus of the caudal mesopallium.

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<figure><img src="image_6.png" /><figcaption>Figure 4: Knowns and unknowns in the field of avian multisensory processing</figcaption></figure>

Avian multisensory processing has been mostly studied in the context of social learning, perception, and motor control. In precocial birds (left, top), filial imprinting highlighted the involvement of the IMM, a multisensory region that receives afferents from all sensory systems and projects to motor areas. In songbirds (left, bottom), it remains to identify which brain network yield young songbirds to learn better copies of their tutor songs when they can directly socially interact. In barn owls (middle, top), the multisensory based detection of preys requires the TeO, a visual area. In songbirds, female preferences for certain song types involve song nuclei (HVC, RA) and nuclei outside of the song system (NCC, RAm). In corvids (right, bottom), songbird species used to assess their higher cognitive abilities, learning of arbitrary audio-visual associations recruits neurons of the NCL, avian analogue of the mammalian prefrontal cortex, which might also be at play for multimodal recognition of conspecifics. In male songbirds (right, top), the cortico-basal ganglia network (LMAN and Area X) is involved in the socially-dependent modulation of the song motor control. See main text for references.
1108 Box 1: What is multisensory integration at the neuronal level?

1109 Multisensory integration generally refers to the set of processes by which redundant and/or
1110 complementary information arriving from different sensory modalities (e.g., vision, audition, touch)
1111 are combined to yield an enriched perceptual experience of multimodal events. This form of
1112 multisensory processing reflects what is commonly reported as cue integration. Yet, multisensory
1113 processing includes other forms of integration such as sensory selection, multisensory based object
1114 recognition and segregation of cross-modal stimuli (Meijer et al., 2019). Sensory selection reflects the
1115 ability to neglect the information from one modality. Multisensory based object recognition involves a
1116 transfer of information from one modality to another during a discrimination task: animals are trained
1117 to identify an object based on a single modality (e.g. touch) and are then tested to identify the same
1118 object using another modality (e.g. sight). Modality segregation occurs when information from one
1119 modality overshadows information from another modality.

1120 While multisensory cue integration relies mostly on bottom-up neuronal processes with a feedforward
1121 transmission from low-level to associative areas, learning and cognitive skills might also modulate
1122 multisensory integration through top-down modulations exerted by higher-level areas. In the present
1123 review, multisensory processes refer not only to multisensory cue integration but also to the whole set
1124 of processes by which information of one modality influences or interacts with the processing of the
1125 specific information of another modality.

1126 Multisensory processes and integration have been quantified using a wide range of experimental
1127 approaches (Stevenson et al., 2014). At the neuronal level, the most commonly implemented method
1128 focuses on single- or multiunit responses, driven by multimodal signals consisting of two or more
1129 modality-specific components presented in a spatiotemporally congruent manner (for more details and
1130 metrics used, see Stevenson et al., 2014). These neuronal responses are often evaluated using spike
1131 counts (sometimes in combination with a spike train analysis) and are compared to responses evoked
1132 by a modality-specific component presented in a temporally and/or spatially incongruent way or to
1133 components presented separately. This comparison presumes a convergence of inputs from multiple
1134 sensory sources onto individual neurons in the brain region of interest. Neuronal responses to
multimodal signals can rely on linear response properties, in which case the responses approximatively correspond to the sum of all responses to unimodal signals (Figure 1). When these responses cannot be explained by responses to unimodal signals because they are higher or lower than expected based on their summation, then this non-linearity is commonly considered as reflecting a multisensory integration. Using spike train analyses, one typically estimates of the mutual information between unimodal or multimodal stimuli and uses the temporal spike patterns to examine whether neurons transmit more information from multisensory stimuli than from a unisensory stimulus (Bizley et al., 2007). Yet, the benefit provided by multisensory integration at the behavioural level may be due to linear properties (Angelaki et al., 2009). Also, certain computational models suggest that both linear and non-linear operations could be achieved by multisensory neurons (Morgan et al., 2008; Ohshiro et al., 2011).

Neurons exhibiting multisensory integration have been studied in the superior colliculus, a midbrain structure that contains multisensory convergence zones (Stein and Stanford, 2008a; Wallace et al., 1998). These neurons guide orienting behavior towards salient stimuli, a form of cue integration that relies on bottom-up processes. Experimental evidences also suggest that these neurons can drive attention through an interplay between bottom-up and top-down mechanisms (Talsma et al., 2010). In this brain structure, responses of multisensory neurons to bimodal congruent stimuli consisting of unimodal stimuli that are aligned in space and temporally synchronized are typically stronger than responses to the unimodal components. Interestingly, bimodal incongruent stimuli that are misaligned in space do not induce this enhancement in responses when compared with the unimodal responses. A depression was also observed in some responses evoked by bimodal incongruent stimuli. This differential effect suggests that congruent bimodal stimuli arise from the same object while incongruent information arise from competing objects. The multisensory integration could thus help bind the different unisensory components into a single unified object.

Enhanced responses to bimodal stimuli that are suitable for target detection have been described in other regions than the superior colliculus, including primary sensory cortices (e.g. Bizley et al., 2007; Kayser et al., 2008; Knöpfel et al., 2019). However, combining stimuli from two different modalities
1162 can manifest differently in different brain regions, including a suppression of responses in primary
1163 sensory cortices (Angelaki et al., 2009; Dehner et al., 2004; Garner and Keller, 2022; Ibrahim et al.,
1164 2016; Iurilli et al., 2012; Lohse et al., 2021b). This effect may allow a sharpening of functional
1165 properties, such as orientation selectivity in V1 or reducing the impact of information from other
1166 modalities, including distracting or self-generated signals, to increase the salience of information from
1167 one specific modality (Ibrahim et al., 2016; Iurilli et al., 2012; Lohse et al., 2021b). It has also been
1168 observed in the context of task learning (Garner and Keller, 2022).

1169 Another methodological approach, measuring local field potentials, is also based on
1170 electrophysiological recordings in the brain region of interest. This has provided complementary
1171 information to the information obtained through single- and multiunit recordings, from subthreshold
1172 mechanisms at the synaptic level and provided insight into how neural populations may encode
1173 multisensory signals. A resetting of ongoing oscillations has been proposed as the possible mechanism
1174 used to amplify sensory signals by optimizing neuronal excitability (Lakatos et al., 2007; van
1175 Atteveldt et al., 2014).