5,555 research outputs found
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
Noise reduction of coincidence detector output by the inferior colliculus of the barn owl
A recurring theme in theoretical work is that integration over populations of similarly tuned neurons can reduce neural noise. However, there are relatively few demonstrations of an explicit noise reduction mechanism in a neural network. Here we demonstrate that the brainstem of the barn owl includes a stage of processing apparently devoted to increasing the signal-to-noise ratio in the encoding of the interaural time difference (ITD), one of two primary binaural cues used to compute the position of a sound source in space. In the barn owl, the ITD is processed in a dedicated neural pathway that terminates at the core of the inferior colliculus (ICcc). The actual locus of the computation of the ITD is before ICcc in the nucleus laminaris (NL), and ICcc receives no inputs carrying information that did not originate in NL. Unlike in NL, the rate-ITD functions of ICcc neurons require as little as a single stimulus presentation per ITD to show coherent ITD tuning. ICcc neurons also displayed a greater dynamic range with a maximal difference in ITD response rates approximately double that seen in NL. These results indicate that ICcc neurons perform a computation functionally analogous to averaging across a population of similarly tuned NL neurons
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-
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Pavlov's dog associative learning demonstrated on synaptic-like organic transistors
In this letter, we present an original demonstration of an associative
learning neural network inspired by the famous Pavlov's dogs experiment. A
single nanoparticle organic memory field effect transistor (NOMFET) is used to
implement each synapse. We show how the physical properties of this dynamic
memristive device can be used to perform low power write operations for the
learning and implement short-term association using temporal coding and spike
timing dependent plasticity based learning. An electronic circuit was built to
validate the proposed learning scheme with packaged devices, with good
reproducibility despite the complex synaptic-like dynamic of the NOMFET in
pulse regime
Virtual Audio - Three-Dimensional Audio in Virtual Environments
Three-dimensional interactive audio has a variety ofpotential uses in human-machine interfaces. After lagging seriously
behind the visual components, the importance of sound is now becoming
increas-ingly accepted.
This paper mainly discusses background and techniques to implement
three-dimensional audio in computer interfaces. A case study of a
system for three-dimensional audio, implemented by the author, is
described in great detail. The audio system was moreover integrated
with a virtual reality system and conclusions on user tests and use
of the audio system is presented along with proposals for future work
at the end of the paper.
The thesis begins with a definition of three-dimensional audio and a
survey on the human auditory system to give the reader the needed
knowledge of what three-dimensional audio is and how human auditory
perception works
Organic electrode coatings for next-generation neural interfaces
Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however, several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes
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