1,088 research outputs found

    A Spiking Neural Network Model of the Medial Superior Olive Using Spike Timing Dependent Plasticity for Sound Localization

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    Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz–1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of ±10° is used. For angular resolutions down to 2.5°, it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance

    A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation

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    Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz-1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of +/-10 degrees is used. For angular resolutions down to 2.5 degrees , it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance

    Using Reinforcement Learning to Attenuate for Stochasticity in Robot Navigation Controllers

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    International audienceBraitenberg vehicles are bio-inspired controllers for sensor-based local navigation of wheeled robots that have been used in multiple real world robotic implementations. The common approach to implement such non-linear control mechanisms is through neural networks connecting sensing to motor action, yet tuning the weights to obtain appropriate closed-loop navigation behaviours can be very challenging. Standard approaches used hand tuned spiking or recurrent neural networks, or learnt the weights of feedforward networks using evolutionary approaches. Recently, Reinforcement Learning has been used to learn neural controllers for simulated Braitenberg vehicle 3a-a bio-inspired model of target seeking for wheeled robots-under the assumption of noiseless sensors. Real sensors, however, are subject to different levels of noise, and multiple works have shown that Braitenberg vehicles work even on outdoor robots, demonstrating that these control mechanisms work in harsh and dynamic environments. This paper shows that a robust neural controller for Braitenberg vehicle 3a can be learnt using policy gradient reinforcement learning in scenarios where sensor noise plays a non negligible role. The learnt controller is robust and tries to attenuate the effects of noise in the closed-loop navigation behaviour of the simulated stochastic vehicle. We compare the neural controller learnt using Reinforcement Learning with a simple hand tuned controller and show how the neural control mechanism outperforms a naïve controller. Results are illustrated through computer simulations of the closed-loop stochastic system

    Financial time series prediction using spiking neural networks

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    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. © 2014 Reid et al

    Binaural sound source localization using machine learning with spiking neural networks features extraction

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    Human and animal binaural hearing systems are able take advantage of a variety of cues to localise sound-sources in a 3D space using only two sensors. This work presents a bionic system that utilises aspects of binaural hearing in an automated source localisation task. A head and torso emulator (KEMAR) are used to acquire binaural signals and a spiking neural network is used to compare signals from the two sensors. The firing rates of coincidence-neurons in the spiking neural network model provide information as to the location of a sound source. Previous methods have used a winner-takesall approach, where the location of the coincidence-neuron with the maximum firing rate is used to indicate the likely azimuth and elevation. This was shown to be accurate for single sources, but when multiple sources are present the accuracy significantly reduces. To improve the robustness of the methodology, an alternative approach is developed where the spiking neural network is used as a feature pre-processor. The firing rates of all coincidence-neurons are then used as inputs to a Machine Learning model which is trained to predict source location for both single and multiple sources. A novel approach that applied spiking neural networks as a binaural feature extraction method was presented. These features were processed using deep neural networks to localise multisource sound signals that were emitted from different locations. Results show that the proposed bionic binaural emulator can accurately localise sources including multiple and complex sources to 99% correctly predicted angles from single-source localization model and 91% from multi-source localization model. The impact of background noise on localisation performance has also been investigated and shows significant degradation of performance. The multisource localization model was trained with multi-condition background noise at SNRs of 10dB, 0dB, and -10dB and tested at controlled SNRs. The findings demonstrate an enhancement in the model performance in compared with noise free training data

    A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

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    This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation

    Neuromorphic auditory computing: towards a digital, event-based implementation of the hearing sense for robotics

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    In this work, it is intended to advance on the development of the neuromorphic audio processing systems in robots through the implementation of an open-source neuromorphic cochlea, event-based models of primary auditory nuclei, and their potential use for real-time robotics applications. First, the main gaps when working with neuromorphic cochleae were identified. Among them, the accessibility and usability of such sensors can be considered as a critical aspect. Silicon cochleae could not be as flexible as desired for some applications. However, FPGA-based sensors can be considered as an alternative for fast prototyping and proof-of-concept applications. Therefore, a software tool was implemented for generating open-source, user-configurable Neuromorphic Auditory Sensor models that can be deployed in any FPGA, removing the aforementioned barriers for the neuromorphic research community. Next, the biological principles of the animals' auditory system were studied with the aim of continuing the development of the Neuromorphic Auditory Sensor. More specifically, the principles of binaural hearing were deeply studied for implementing event-based models to perform real-time sound source localization tasks. Two different approaches were followed to extract inter-aural time differences from event-based auditory signals. On the one hand, a digital, event-based design of the Jeffress model was implemented. On the other hand, a novel digital implementation of the Time Difference Encoder model was designed and implemented on FPGA. Finally, three different robotic platforms were used for evaluating the performance of the proposed real-time neuromorphic audio processing architectures. An audio-guided central pattern generator was used to control a hexapod robot in real-time using spiking neural networks on SpiNNaker. Then, a sensory integration application was implemented combining sound source localization and obstacle avoidance for autonomous robots navigation. Lastly, the Neuromorphic Auditory Sensor was integrated within the iCub robotic platform, being the first time that an event-based cochlea is used in a humanoid robot. Then, the conclusions obtained are presented and new features and improvements are proposed for future works.En este trabajo se pretende avanzar en el desarrollo de los sistemas de procesamiento de audio neuromórficos en robots a través de la implementación de una cóclea neuromórfica de código abierto, modelos basados en eventos de los núcleos auditivos primarios, y su potencial uso para aplicaciones de robótica en tiempo real. En primer lugar, se identificaron los principales problemas a la hora de trabajar con cócleas neuromórficas. Entre ellos, la accesibilidad y usabilidad de dichos sensores puede considerarse un aspecto crítico. Los circuitos integrados analógicos que implementan modelos cocleares pueden no pueden ser tan flexibles como se desea para algunas aplicaciones específicas. Sin embargo, los sensores basados en FPGA pueden considerarse una alternativa para el desarrollo rápido y flexible de prototipos y aplicaciones de prueba de concepto. Por lo tanto, en este trabajo se implementó una herramienta de software para generar modelos de sensores auditivos neuromórficos de código abierto y configurables por el usuario, que pueden desplegarse en cualquier FPGA, eliminando las barreras mencionadas para la comunidad de investigación neuromórfica. A continuación, se estudiaron los principios biológicos del sistema auditivo de los animales con el objetivo de continuar con el desarrollo del Sensor Auditivo Neuromórfico (NAS). Más concretamente, se estudiaron en profundidad los principios de la audición binaural con el fin de implementar modelos basados en eventos para realizar tareas de localización de fuentes sonoras en tiempo real. Se siguieron dos enfoques diferentes para extraer las diferencias temporales interaurales de las señales auditivas basadas en eventos. Por un lado, se implementó un diseño digital basado en eventos del modelo Jeffress. Por otro lado, se diseñó una novedosa implementación digital del modelo de codificador de diferencias temporales y se implementó en FPGA. Por último, se utilizaron tres plataformas robóticas diferentes para evaluar el rendimiento de las arquitecturas de procesamiento de audio neuromórfico en tiempo real propuestas. Se utilizó un generador central de patrones guiado por audio para controlar un robot hexápodo en tiempo real utilizando redes neuronales pulsantes en SpiNNaker. A continuación, se implementó una aplicación de integración sensorial que combina la localización de fuentes de sonido y la evitación de obstáculos para la navegación de robots autónomos. Por último, se integró el Sensor Auditivo Neuromórfico dentro de la plataforma robótica iCub, siendo la primera vez que se utiliza una cóclea basada en eventos en un robot humanoide. Por último, en este trabajo se presentan las conclusiones obtenidas y se proponen nuevas funcionalidades y mejoras para futuros trabajos
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