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

Spiking memristor logic gates are a type of time-variant perceptron

Memristors are low-power memory-holding resistors thought to be useful for neuromophic computing, which can compute via spike-interactions mediated through the device's short-term memory. Using interacting spikes, it is possible to build an AND gate that computes OR at the same time, similarly a full adder can be built that computes the arithmetical sum of its inputs. Here we show how these gates can be understood by modelling the memristors as a novel type of perceptron: one which is sensitive to input order. The memristor's memory can change the input weights for later inputs, and thus the memristor gates cannot be accurately described by a single perceptron, requiring either a network of time-invarient perceptrons or a complex time-varying self-reprogrammable perceptron. This work demonstrates the high functionality of memristor logic gates, and also that the addition of theasholding could enable the creation of a standard perceptron in hardware, which may have use in building neural net chips.Comment: 8 pages, 3 figures. Poster presentation at a conferenc

Two-dimensional brain microtubule structures behave as memristive devices

Microtubules (MTs) are cytoskeletal structures that play a central role in a variety of cell functions including cell division and cargo transfer. MTs are also nonlinear electrical transmission lines that produce and conduct electrical oscillations elicited by changes in either electric field and/or ionic gradients. The oscillatory behavior of MTs requires a voltage-sensitive gating mechanism to enable the electrodiffusional ionic movement through the MT wall. Here we explored the electrical response of non-oscillating rat brain MT sheets to square voltage steps. To ascertain the nature of the possible gating mechanism, the electrical response of non-oscillating rat brain MT sheets (2D arrays of MTs) to square pulses was analyzed under voltage-clamping conditions. A complex voltage-dependent nonlinear charge movement was observed, which represented the summation of two events. The first contribution was a small, saturating, voltage-dependent capacitance with a maximum charge displacement in the range of 4 fC/μm2. A second, major contribution was a non-saturating voltage-dependent charge transfer, consistent with the properties of a multistep memristive device. The memristive capabilities of MTs could drive oscillatory behavior, and enable voltage-driven neuromorphic circuits and architectures within neurons.Fil: Cantero, María del Rocío. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet Noa Sur. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo. - Universidad Nacional de Santiago del Estero. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo.; ArgentinaFil: Perez, Paula L.. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet Noa Sur. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo. - Universidad Nacional de Santiago del Estero. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo.; ArgentinaFil: Scarinci, María Noelia. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet Noa Sur. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo. - Universidad Nacional de Santiago del Estero. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo.; ArgentinaFil: Cantiello, Horacio Fabio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet Noa Sur. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo. - Universidad Nacional de Santiago del Estero. Instituto Multidisciplinario de Salud, Tecnologia y Desarrollo.; Argentin

Biomimetic Application of Ion-Conducting-Based Memristive Devices in Spike-Timing-Dependent-Plasticity

The design and synthesis of artificial learning systems has been aided by the study of biological learning systems. Classic biological learning is driven by the strengthening and weakening of the synapses that connect neurons within the brain through a phenomenon known as Spike-Timing-Dependent-Plasticity. That is, synaptic connectivity between neurons is modulated by the relative timing of their spiking outputs. Similarly, neuromorphic computing architectures can implement a mesh of artificial neurons interconnected by a network of artificial synapses to mimic the learning behaviors found in nature. Memristors, two-terminal devices whose resistance can be programmed as a function of voltage and current, offer a promising biomimetic solution for a hardware-based artificial synapse. This work focuses on characterizing the switching behavior of an ion-conducting, chalcogenide-based resistive memory in a test environment emulating the behavior of a two-neuron, single-synapse neuromorphic circuit to demonstrate learning at speeds significantly faster than those found in biological synapses. The results from this study show that the ion-conducting memristors used in this work exhibit effective learning at time scales ranging over several orders of magnitude: from the biologically-relevant millisecond region to the faster-than-nature nanosecond region

Evolution of plastic learning in spiking networks via memristive connections

This paper presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and interneural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of: 1) linear resistors, and 2) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks. © 2012 IEEE