Encoding and Decoding Dynamic Sensory Signals with Recurrent Neural Networks: An Application of Conceptors to Birdsongs

In a constantly changing environment the brain has to make sense of dynamic patterns of sensory input. These patterns can refer to stimuli with a complex and hierarchical structure which has to be inferred from the neural activity of sensory areas in the brain. Such areas were found to be locally recurrently structured as well as hierarchically organized within a given sensory domain. While there is a great body of work identifying neural representations of various sensory stimuli at different hierarchical levels, less is known about the nature of these representations. In this work, we propose a model that describes a way to encode and decode sensory stimuli based on the activity patterns of multiple, recurrently connected neural populations with different receptive fields. We demonstrate the ability of our model to learn and recognize complex, dynamic stimuli using birdsongs as exemplary data. These birdsongs can be described by a 2-level hierarchical structure, i.e. as sequences of syllables. Our model matches this hierarchy by learning single syllables on a first level and sequences of these syllables on a top level. Model performance on recognition tasks is investigated for an increasing number of syllables or songs to recognize and compared to state-of-the-art machine learning approaches. Finally, we discuss the implications of our model for the understanding of sensory pattern processing in the brain. We conclude that the employed encoding and decoding mechanisms might capture general computational principles of how the brain extracts relevant information from the activity of recurrently connected neural populations.