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In natural auditory environments, acoustic signals originate from thetemporal superimposition of different sound sources. The problem of inferringindividual sources from ambiguous mixtures of sounds is known as blind sourcedecomposition. Experiments on humans have demonstrated that the auditory systemcan identify sound sources as repeating patterns embedded in the acousticinput. Source repetition produces temporal regularities that can be detectedand used for segregation. Specifically, listeners can identify sounds occurringmore than once across different mixtures, but not sounds heard only in a singlemixture. However, whether such a behaviour can be computationally modelled hasnot yet been explored. Here, we propose a biologically inspired computationalmodel to perform blind source separation on sequences of mixtures of acousticstimuli. Our method relies on a somatodendritic neuron model trained with aHebbian-like learning rule which can detect spatio-temporal patterns recurringin synaptic inputs. We show that the segregation capabilities of our model arereminiscent of the features of human performance in a variety of experimentalsettings involving synthesized sounds with naturalistic properties.Furthermore, we extend the study to investigate the properties of segregationon task settings not yet explored with human subjects, namely natural soundsand images. Overall, our work suggests that somatodendritic neuron models offera promising neuro-inspired learning strategy to account for the characteristicsof the brain segregation capabilities as well as to make predictions on yetuntested experimental settings.

Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons