Activity‐dependent plasticity of synaptic connections is a hallmark of the mammalian brain and represents a key mechanism for rewiring neural circuits during development, experience‐dependent plasticity, and brain disorders. Cellular models of memory, such as long‐term potentiation and long‐term depression, share common principles to memory consolidation. As for memory, the maintenance of synaptic plasticity is dependent on the synthesis of de novo protein synthesis. The synaptic‐tagging and capture hypothesis states that the maintenance of synaptic plasticity is dependent on the interplay between input‐specific synaptic tags and the allocation or capture of plasticity‐related proteins (PRPs) at activated synapses. The setting of the synaptic tag and the capture of PRPs are independent processes that can occur separated in time and different groups of activated synapses. How are these two processes orchestrated in time and space? Here, we discuss the synaptic‐tagging and capture hypothesis in the light of neuronal compartmentalization models and address the role of actin as a putative synaptic tag. If different groups of synapses interact by synaptic‐tagging and capture mechanisms, understanding the spatial rules of such interaction is key to define the relevant neuronal compartment. We also discuss how actin modulation can allow an input‐specific capture of PRPs and try to conciliate the temporal dynamics of synaptic actin with the maintenance of plasticity. Understanding how multiple synapses interact in time and space is fundamental to predict how neurons integrate information and ultimately how memory is acquired.

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Animals can use social information to detect threat in the environment. In particular, social learning allows animals to learn about dangers without incurring in the costs of trial‐and‐error learning. In zebrafish, both chemical and visual social cues elicit an innate alarm response, which consists of erratic movement followed by freezing behavior. Injured zebrafish release an alarm substance from their skin that elicits the alarm response. Similarly, the sight of conspecifics displaying the alarm response can also elicit the expression of this response in observers. In this study, we investigated if these social cues of danger can also be used by zebrafish as unconditioned stimulus (US) in learning. We found that only the chemical cue was effective in the social fear conditioning. We suggest that this differential efficacy of social cues results from the fact that the alarm cue is a more reliable indicator of threat, than the sight of an alarmed conspecific. Therefore, although multiple social cues may elicit innate responses not all have been evolutionarily co‐opted to act as US in associative learning. Furthermore, the use of the expression of the immediate early genes as markers of neuronal activity showed that chemical social fear conditioning is paralleled by a differential activation of the olfactory bulbs and by a different pattern of functional connectivity across brain regions involved in olfactory processing.

Group living animals can use social and asocial cues to predict the presence of a reward or a punishment in the environment through associative learning. The degree to which social and asocial learning share the same mechanisms is still a matter of debate, and, so far, studies investigating the neuronal basis of these two types of learning are scarce and have been restricted to primates, including humans, and rodents. Here we have used a Pavlovian fear conditioning paradigm in which a social (fish image) or an asocial (circle image) conditioned stimulus (CS) have been paired with an unconditioned stimulus (US=food), and we have used the expression of the immediate early gene c-fos to map the neural circuits associated with social and asocial learning. Our results show that the learning performance is similar with social (fish image) and asocial (circle image) CSs. However, the brain regions involved in each learning type are distinct. Social learning is associated with an increased expression of c-fos in olfactory bulbs, ventral zone of ventral telencephalic area, ventral habenula and ventromedial thalamus, whereas asocial learning is associated with a decreased expression of c-fos in dorsal habenula and anterior tubercular nucleus. Using egonetworks, we further show that each learning type has an associated pattern of functional connectivity across brain regions. Moreover, a community analysis of the network data reveals four segregated functional submodules, which seem to be associated with different cognitive functions involved in the learning tasks: a generalized attention module, a visual response module, a social stimulus integration module and a learning module. Together, these results suggest that, although there are localized differences in brain activity between social and asocial learning, the two learning types share a common learning module and social learning also recruits a specific social stimulus integration module. Therefore, our results support the occurrence of a common general-purpose learning module, that is differentially modulated by localized activation in social and asocial learning.