Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

Neglect and the Kaleidoscopic Mind: Psychology and Mental Health in Contemporary Art

This paper seeks to explore the broad question of whether and how art can be applied to medical therapeutic practices. As part of this research, the paper outlines an ongoing project, exemplifying this combined approach, which seeks to improve function in stroke patients. We reviewed previous collaborations between art and psychology dating back to the 1960s, employing methods ranging from simple, analog, haptic interfaces to the contemporary potential of machine learning to improve brain function. We then outline an ongoing project employing machine learning and multisensory stimulation to improve function in stroke patients, which are being run in collaboration with Klinik Lippoldsberg, Germany. We discuss the possibility that these same approaches may also be applied to healthy people as an open-ended inquiry into consciousness and mental optimization. It is hoped that these approaches will be beneficial to the medical community, but also equally broaden the reach and context of contemporary art, which is so often marginalized within institutions that are not readily accessible to or in communication with other disciplines.

Self-Crossing Memristive Pinched Hystereses in Autonomous Implicit Models

This paper shows that autonomous implicit ODEs (based on planar lemniscates) are interesting models, yielding pinched self-crossing hystereses that are intrinsic features of all memristive elements in nanoscale electronics. Each model has a folded saddle that allows for an oscillating trajectory to traverse different sides of singularity. The models considered in this paper are autonomous and therefore different from typical input–state–output models considered thus far. The models preserve the usual properties of memristive elements. For example, the area enclosed by the pinched hystereses decreases with the increased frequency of oscillations. The same-time instant zero-crossing property is also satisfied provided that certain conditions are met. Another novel aspect of this paper is the fact that the autonomous models are based on various planar lemniscates (of Gerono, Devil and Bernoulli) which can be nonlinearly transformed to model pinched hystereses of various shapes. The proposed models are differentiable and the use of the sign and absolute value terms, typical in modeling of memristive elements, is avoided. Several simulation results are included and two simple analog circuits having the pinched hysteretic characteristics of mem-inductors and mem-capacitors are proposed.

Realization Approach for Sinusoidal Signal Generation and Circuit with Easy Control

A new approach for realizing sinusoidal signal with quadrature property is proposed, which employs simple analog building blocks and facilitates easy tuning of the oscillation frequency, through a gain factor. The proposed approach is used for realizing a novel quadrature oscillator circuit, which requires three current feedback operational amplifiers and passive components. The proposed circuit provides outputs at low impedance terminals, and benefits from easy control over the frequency of oscillation (FO), which depends on resistive ratio, rather than absolute resistor values. The frequency control is also independent of the condition of oscillation (CO). The nonideal effects and the parasitic studies are presented. The verification of the proposed realization scheme for quadrature oscillators and the new circuit is carried out through both simulation studies and experimental results, using the commercially available chips.

Diseño de un Prototipo de Electroestimulación Neuromuscular de Baja Frecuencia

Objective. Design a low frequency Neuromuscular Electrostimulation prototype with modifiable digital parameters through the Arduino platform. Methodology: The following work shows the design of a low-frequency neuromuscular electrostimulation equipment prototype with similar characteristics to conventional devices but with a unique design in its programming, this prototype allows modifying the parameters of intensity, time, pulse duration and Frequency through the Arduino platform, this HARDWARE is based on simple analog and digital boards that allow reading input and output data on the device. This prototype emits a symmetrical biphasic current of rectangular pulses and although there is a great variety of currents within the Neuromuscular Electrostimulation (EENM) focused on improving and maximizing the activation of a muscle, generally the symmetric biphasic pulses are better tolerated to the passage of a current through subcutaneous tissue, with greater effectiveness in depolarization of thick fibers of intact nerves. Contribution: To offer alternatives for the management of digital devices in electrotherapy.