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  • Computer Music Meets Unconventional Computing:Towards Sound Synthesis with In Vitro Neuronal Networks
  • Eduardo R. Miranda, Larry Bull, François Gueguen, and Ivan S. Uroukov

We are interested in exploring ways in which unconventional modes of computation may provide new directions for future developments in computer music. Research into unconventional computing (Calude, Casti, and Dinneen 1998) addresses computational paradigms other than the standard von Neumann architecture, which has prevailed in computing since the 1940s. This article presents an investigation into the feasibility of synthesizing sounds with hybrid wetware-silicon devices using in vitro neuronal networks.

The field of computer music has evolved in tandem with the field of computer science. Computers have been programmed to play music as early as the beginning of the 1950s, when the CSIR Mk1 computer in Australia was programmed to play popular musical melodies (Doornbusch 2005). The Illiac Suite for String Quartet, written in the USA in the late 1950s by composer Lejaren Hiller and mathematician Leonard Isaacson (Hiller and Isaacson 1959), is often cited as the first piece of music involving materials generated by a computer. (The fourth movement was generated using a Markov chain.) Currently, the computer is ubiquitous in many aspects of music, ranging from software for musical composition and production, to systems for distribution of music on the Internet. Therefore, it is likely that future developments in computer science will continue to have an impact on music.

New computational paradigms based on or inspired by the principles of information processing in physical, chemical, and biological systems are promising new venues for the development of new types of computers, and these new ideas may eventually supersede classical paradigms. For instance, it has been reported that reaction-diffusion chemical computers have been capable of performing a number of complex computational tasks, including the design of logical circuits (Steinbock, Toth, and Showalter 1996) and image processing (Kuhnert, Agladze, and Krinsky 1989).

In short, unconventional computing takes computation (or part of it) into the real world, thereby harnessing the immense parallelism and non-algorithmic openness of physical systems. There has been a growing interest in research into the development of hybrid wetware-silicon devices for non-linear computations using cultured brain cells (DeMarse et al. 2001; Potter et al. 2004; Bontorin et al. 2007; Bull and Uroukov 2007; Novellino et al. 2007). The ambition is to harness the intricate dynamics of in vitro neuronal networks to perform computations. Researchers have already mastered techniques to culture tens of thousands of brain cells (neurons and glia) in vitro, on the scale of a few square millimeters, in a mini Petri-like dish with embedded electrodes. Such a system is referred to as a multi-electrode array (MEA) device (see Figure 1). The electrodes can detect neural activity of aggregates of cells and stimulate those cells with electrical pulses (see Figure 2). An MEA can record extra-cellular neural signals fast enough to detect the firing of thousands of nearby neurons as micro-voltage spikes. Thus, the activity of multiple aggregates of neurons can be observed in parallel, and neuronal network phenomena can be studied. Supplying [End Page 9] electrical stimulation through the multiple electrodes typically induces widespread neural activity.

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Figure 1.

A typical MEA used to stimulate and record electrical activity of cultured brain cells on the surface of an array of electrodes. (Image printed with permission of Multi-channel Systems [].)

In vitro cultures of brain cells display a strong disposition to form synapses, even more so if subjected to electrical stimulation. It is well known that in vitro cells spontaneously branch out, even if left to themselves without external input other than nutrients in the dish. They establish connections with their neighbors and begin to communicate within days, demonstrating an inherent bias to form networks. Dissociated neurons begin to form connections within a few hours, and an elaborate and spontaneously active living neuronal network is established within a few days. After one month in culture, the development of these networks becomes relatively stable and is characterized by spontaneous bursts of activity (Kamioka et al. 1996). Potter and DeMarse (2001) have developed methods to maintain cultures of...


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