Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation

We undertook a study of the use of a memristor network for music generation, making use of the memristor's memory to go beyond the Markov hypothesis. Seed transition matrices are created and populated using memristor equations, and which are shown to generate musical melodies and change in style over time as a result of feedback into the transition matrix. The spiking properties of simple memristor networks are demonstrated and discussed with reference to applications of music making. The limitations of simulating composing memristor networks in von Neumann hardware is discussed and a hardware solution based on physical memristor properties is presented.Comment: 22 pages, 13 pages, conference pape

Networks: open, closed or complex. Connecting philosophy, design and innovation, part 3

This is the third and final paper of a series bringing a philosophical investigation to matters of design and innovation. With the others examining: first, the urges to reconsider innovation from a creative, specifically design, direction (‘Beyond Success’); and second, the type of dynamic innovation that may be thus reconsidered (‘Ecstatic Innovation’); this paper will investigate a way of constructing this type of design-driven innovation. It will begin by looking at the networks that can be created to deliver a dynamic, continually innovative innovation and will start by considering two concepts of network: the open and the closed. While there seems to be an easy distinction to be made between open and closed, and its mapping onto similarly convenient ideas of good and bad, I hope to show that this is not the case. The complexity of networked forms of organisation demand that we bring to them a complexity of thought that comes from philosophy. Nevertheless, such an account will also need to engage with discourses from other disciplinary areas: notably organisational theory, innovation management and design. The outcome is of importance to thinking the organisational structures in which innovation is managed

Incorporating characteristics of human creativity into an evolutionary art algorithm (journal article)

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically

Complex learning communities

A new breed of learning community which is driven by the need to generate learning, creativity and economic capacity is emerging as a result of the demands of the Information Society. Radical heterogeneity and multiple drivers make these learning communities significantly different from previously identified learning communities such as corporate Communities of Practice or Virtual Learning Communities. If full benefit is to be realised from such Complex Learning Communities (CLCs), then better understanding of their complex behaviour and methods of maximising their effectiveness are required. This short paper presents an overview of CLCs and reports on the development of a research agenda designed to address the identified gaps in knowledge

Incorporating characteristics of human creativity into an evolutionary art algorithm

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically

The artisan and the artist. Innovation enables transformation

Technologies Excellence Group, for theCurriculum for Excellence Group for SG (commissioned by/for Mike Russell-Cabinet Secy on Education

Chimeras in Leaky Integrate-and-Fire Neural Networks: Effects of Reflecting Connectivities

The effects of nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which assimilate the exchange of electrical signals between neurons. Earlier investigations have demonstrated that non-local and hierarchical network connectivity often induces complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are non-locally linked with positive diffusive coupling in a ring architecture the system splits into a number of alternating domains. Half of these domains contain elements, whose potential stays near the threshold, while they are interrupted by active domains, where the elements perform regular LIF oscillations. The active domains move around the ring with constant velocity, depending on the system parameters. The idea of introducing reflecting non-local coupling in LIF networks originates from signal exchange between neurons residing in the two hemispheres in the brain. We show evidence that this connectivity induces novel complex spatial and temporal structures: for relatively extensive ranges of parameter values the system splits in two coexisting domains, one domain where all elements stay near-threshold and one where incoherent states develop with multileveled mean phase velocity distribution.Comment: 12 pages, 12 figure