Interest in statistical learning in developmental studies stems from the observation that 8-month-olds were able to extract words from a monotone speech stream solely using the transition probabilities (TP) between syllables (Saffran et al., 1996). A simple mechanism was thus part of the human infant’s toolbox for discovering regularities in language. Since this seminal study, observations on statistical learning capabilities have multiplied across domains and species, challenging the hypothesis of a dedicated mechanism for language acquisition. Here, we leverage the two dimensions conveyed by speech –speaker identity and phonemes– to examine (1) whether neonates can compute TPs on one dimension despite irrelevant variation on the other and (2) whether the linguistic dimension enjoys an advantage over the voice dimension. In two experiments, we exposed neonates to artificial speech streams constructed by concatenating syllables while recording EEG. The sequence had a statistical structure based either on the phonetic content, while the voices varied randomly (Experiment 1) or on voices with random phonetic content (Experiment 2). After familiarisation, neonates heard isolated duplets adhering, or not, to the structure they were familiarised with. In both experiments, we observed neural entrainment at the frequency of the regularity and distinct Event-Related Potentials (ERP) to correct and incorrect duplets, highlighting the universality of statistical learning mechanisms and suggesting it operates on virtually any dimension the input is factorised. However, only linguistic duplets elicited a specific ERP component, potentially an N400 precursor, suggesting a lexical stage triggered by phonetic regularities already at birth. These results show that, from birth, multiple input regularities can be processed in parallel and feed different higher-order networks.

At many vertebrate synapses, presynaptic functions are tuned by expression of different Ca_v2 channels. Most invertebrate genomes contain only one Ca_v2 gene. The Drosophila Ca_v2 homolog, cacophony (cac), induces synaptic vesicle release at presynaptic active zones (AZs). We hypothesize that Drosophila cac functional diversity is enhanced by two mutually exclusive exon pairs that are not conserved in vertebrates, one in the voltage sensor and one in the loop binding Ca_β and G_βγ subunits. We find that alternative splicing in the voltage sensor affects channel activation voltage. Only the isoform with the higher activation voltage localizes to AZs at the glutamatergic Drosophila larval neuromuscular junction and is imperative for normal synapse function. By contrast, alternative splicing at the other alternative exon pair tunes multiple aspects of presynaptic function. While expression of one exon yields normal transmission, expression of the other reduces channel number in the AZ and thus release probability. This also abolishes presynaptic homeostatic plasticity. Moreover, reduced channel number affects short-term plasticity, which is rescued by increasing the external calcium concentration to match release probability to control. In sum, in Drosophila alternative splicing provides a mechanism to regulate different aspects of presynaptic functions with only one Ca_v2 gene.