Monitor aer generator#

In this work novel speech recognition and audio processing systems based on a spiking artificial cochlea and neural networks are proposed and implemented. Therefore, our neuromorphic-based burst detection paves the road to future implementations for real-time neuroprosthetic applications. Similar results in terms of number of detected events, mean burst duration and correlation as current state-of-the-art approaches were obtained with the proposed system, also benefiting from its lower power consumption and computational latency. This data-driven approach was compared with different conventional spike-based and raw-based burst detection methods, addressing some of their drawbacks, such as being able to detect both high and low frequency events and working in an online manner. The output of the sensor is sent to a trained Spiking Neural Network implemented on a SpiNNaker board that discerns between bursting and non-bursting activity. The system consists of a Neuromorphic Auditory Sensor, which converts the input signal obtained from electrophysiological recordings into spikes and decomposes them into different frequency bands. As an alternative to conventional techniques, a novel neuromorphic approach for real-time burst detection is proposed and tested on acquisitions from in vitro cultures. However, none of the burst detection methods that can be found in the literature have been widely adopted for this task. The correct identification of burst events is crucial in many scenarios, ranging from basic neuroscience to biomedical applications. It also evidences the suitable use of AER as a communication protocol between processing and actuation.

Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). Experimental results reveal the viability of this spike-based controller. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol.

Monitor aer generator#

The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has and finally the actuation layer to supply the spikes to the robot (using PFM).

This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. This tool could aid cardiologists and primary care physicians in the auscultation process, improving the decision making task and reducing type-I and type-II errors. The best results have been obtained with a modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%, PhysioNet/CinC Challenge 2016 score: 0.9416). These images have been used to train and test different convolutional neural network architectures. These samples are segmented and preprocessed using the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after that, sonogram images with the same size are generated.

For this purpose, different networks have been trained with the heart murmur information contained in heart sound recordings obtained from nine different heart sound databases sourced from multiple research groups. In this paper, the authors present a novel convolutional neural network based tool for classifying between healthy people and pathological patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into frequency bands in real time.

However, the accuracy rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients are sent home without medication or treatment) errors made. These heart sounds can either be innocent, which are harmless, or abnormal, which may be a sign of a more serious heart condition. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world.