870 research outputs found
Design of a silicon cochlea system with biologically faithful response
This paper presents the design and simulation results of a silicon cochlea system that has closely similar behavior as the real cochlea. A cochlea filter-bank based on the improved three-stage filter cascade structure is used to model the frequency decomposition function of the basilar membrane; a filter tuning block is designed to model the adaptive response of the cochlea; besides, an asynchronous event-triggered spike codec is employed as the system interface with bank-end spiking neural networks. As shown in the simulation results, the system has biologically faithful frequency response, impulse response, and active adaptation behavior; also the system outputs multiple
band-pass channels of spikes from which the original sound input can be recovered. The proposed silicon cochlea is feasible for analog VLSI implementation so that it not only emulates the way that sounds are preprocessed in human ears but also is able match the compact physical size of a real cochlea
Neuromorphic analogue VLSI
Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do
Sound Recognition System Using Spiking and MLP Neural Networks
In this paper, we explore the capabilities of a sound classification
system that combines a Neuromorphic Auditory System for feature extraction
and an artificial neural network for classification. Two models of neural network
have been used: Multilayer Perceptron Neural Network and Spiking Neural
Network. To compare their accuracies, both networks have been developed and
trained to recognize pure tones in presence of white noise. The spiking neural
network has been implemented in a FPGA device. The neuromorphic auditory
system that is used in this work produces a form of representation that is analogous
to the spike outputs of the biological cochlea. Both systems are able to distinguish
the different sounds even in the presence of white noise. The recognition system
based in a spiking neural networks has better accuracy, above 91 %, even when
the sound has white noise with the same power.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
A silicon model of auditory localization
The barn owl accurately localizes sounds in the azimuthal plane, using interaural time difference as a cue. The time-coding pathway in the owl's brainstem encodes a neural map of azimuth, by processing interaural timing information. We have built a silicon model of the time-coding pathway of the owl. The integrated circuit models the structure as well as the function of the pathway; most subcircuits in the chip have an anatomical correlate. The chip computes all outputs in real time, using analog, continuous-time processing
A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach
This paper presents a new architecture, design
flow, and field-programmable gate array (FPGA) implementation
analysis of a neuromorphic binaural auditory sensor, designed
completely in the spike domain. Unlike digital cochleae that
decompose audio signals using classical digital signal processing
techniques, the model presented in this paper processes information
directly encoded as spikes using pulse frequency modulation
and provides a set of frequency-decomposed audio information
using an address-event representation interface. In this case,
a systematic approach to design led to a generic process for
building, tuning, and implementing audio frequency decomposers
with different features, facilitating synthesis with custom features.
This allows researchers to implement their own parameterized
neuromorphic auditory systems in a low-cost FPGA in order to
study the audio processing and learning activity that takes place
in the brain. In this paper, we present a 64-channel binaural
neuromorphic auditory system implemented in a Virtex-5 FPGA
using a commercial development board. The system was excited
with a diverse set of audio signals in order to analyze its response
and characterize its features. The neuromorphic auditory system
response times and frequencies are reported. The experimental
results of the proposed system implementation with 64-channel
stereo are: a frequency range between 9.6 Hz and 14.6 kHz
(adjustable), a maximum output event rate of 2.19 Mevents/s,
a power consumption of 29.7 mW, the slices requirements
of 11 141, and a system clock frequency of 27 MHz.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
Interfacing PDM sensors with PFM spiking systems: application for Neuromorphic Auditory Sensors
In this paper we present a sub-system to convert
audio information from low-power MEMS microphones with
pulse density modulation (PDM) output into rate coded spike
streams. These spikes represent the input signal of a Neuromorphic
Auditory Sensor (NAS), which is implemented with Spike
Signal Processing (SSP) building blocks. For this conversion, we
have designed a HDL component for FPGA able to interface
with PDM microphones and converts their pulses to temporal
distributed spikes following a pulse frequency modulation (PFM)
scheme with an accurate configurable Inter-Spike-Interval. The
new FPGA component has been tested in two scenarios, first as a
stand-alone circuit for its characterization, and then it has been
integrated with a full NAS design to verify its behavior. This
PDM interface demands less than 1% of a Spartan 6 FPGA
resources and has a power consumption below 5mW.Ministerio de Economía y Competitividad TEC2016-77785-
A Comprehensive Three-Dimensional Model of the Cochlea
The human cochlea is a remarkable device, able to discern extremely small
amplitude sound pressure waves, and discriminate between very close
frequencies. Simulation of the cochlea is computationally challenging due to
its complex geometry, intricate construction and small physical size. We have
developed, and are continuing to refine, a detailed three-dimensional
computational model based on an accurate cochlear geometry obtained from
physical measurements. In the model, the immersed boundary method is used to
calculate the fluid-structure interactions produced in response to incoming
sound waves. The model includes a detailed and realistic description of the
various elastic structures present.
In this paper, we describe the computational model and its performance on the
latest generation of shared memory servers from Hewlett Packard. Using compiler
generated threads and OpenMP directives, we have achieved a high degree of
parallelism in the executable, which has made possible several large scale
numerical simulation experiments that study the interesting features of the
cochlear system. We show several results from these simulations, reproducing
some of the basic known characteristics of cochlear mechanics.Comment: 22 pages, 5 figure
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach
Speech recognition has become an important task
to improve the human-machine interface. Taking into account
the limitations of current automatic speech recognition systems,
like non-real time cloud-based solutions or power demand,
recent interest for neural networks and bio-inspired systems has
motivated the implementation of new techniques.
Among them, a combination of spiking neural networks and
neuromorphic auditory sensors offer an alternative to carry
out the human-like speech processing task. In this approach,
a spiking convolutional neural network model was implemented,
in which the weights of connections were calculated by training
a convolutional neural network with specific activation functions,
using firing rate-based static images with the spiking information
obtained from a neuromorphic cochlea.
The system was trained and tested with a large dataset
that contains ”left” and ”right” speech commands, achieving
89.90% accuracy. A novel spiking neural network model has been
proposed to adapt the network that has been trained with static
images to a non-static processing approach, making it possible
to classify audio signals and time series in real time.Ministerio de Economía y Competitividad TEC2016-77785-
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