Translation from neurobiological data to music parameters

Composers have explored different ways to use biological information for the realization of music. Throughout the decades, biological findings have been repeatedly indicated as a source of inspiration or a reservoir of extramusical material for musical composition. More radical and fertile are attempts to produce music systematically using biological data in processes called data sonification or biofeedback techniques. Presented here is a novel strategy of translation where populations of neurobiological data are converted into relational structures from which sound objects are generated by flexible and homogeneous control of the sound parameters. All brain data originate from experiments performed with standard anatomical and physiological techniques, and results of studies based on these experimental materials have already been published. During the translation processes, the information for every sound parameter (such as pitch, duration, envelope, and dynamics) is never derived from fixed transcriptions of data properties. Rather, the space and/or the time interrelations of data populations are used to obtain indexes for sound construction. In this way, equivalent sets of information are exploited to model, or sculpt, the different parameters of sound objects. Three examples from the last decade's personal productions are given. The first refers to the microformal aspects of sound aggregation and is based on data from a microstimulation experiment in the motor cortex. The second describes the earliest translation process developed for live performance with conventional instruments and is based on experiments using a conventional tract tracing technique to compare selected spinal-projecting cell populations in two differently organized brains. The third outlines a recent music production for three pianos based on data from experiments using the multiple fluorescent tract-tracing technique to simultaneously label different populations of thalamocortical neurons. The approach here described can potentially contribute to a unitary view of the different sound parameters and of the micro- and macroformal aspects of the compositional process.