1,007 research outputs found
Neuromorphic Auditory Perception by Neural Spiketrum
Neuromorphic computing holds the promise to achieve the energy efficiency and
robust learning performance of biological neural systems. To realize the
promised brain-like intelligence, it needs to solve the challenges of the
neuromorphic hardware architecture design of biological neural substrate and
the hardware amicable algorithms with spike-based encoding and learning. Here
we introduce a neural spike coding model termed spiketrum, to characterize and
transform the time-varying analog signals, typically auditory signals, into
computationally efficient spatiotemporal spike patterns. It minimizes the
information loss occurring at the analog-to-spike transformation and possesses
informational robustness to neural fluctuations and spike losses. The model
provides a sparse and efficient coding scheme with precisely controllable spike
rate that facilitates training of spiking neural networks in various auditory
perception tasks. We further investigate the algorithm-hardware co-designs
through a neuromorphic cochlear prototype which demonstrates that our approach
can provide a systematic solution for spike-based artificial intelligence by
fully exploiting its advantages with spike-based computation.Comment: This work has been submitted to the IEEE for possible publicatio
An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing
The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks
Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?
Spiking neural networks (SNNs) equipped with latency coding and spike-timing
dependent plasticity rules offer an alternative to solve the data and energy
bottlenecks of standard computer vision approaches: they can learn visual
features without supervision and can be implemented by ultra-low power hardware
architectures. However, their performance in image classification has never
been evaluated on recent image datasets. In this paper, we compare SNNs to
auto-encoders on three visual recognition datasets, and extend the use of SNNs
to color images. The analysis of the results helps us identify some bottlenecks
of SNNs: the limits of on-center/off-center coding, especially for color
images, and the ineffectiveness of current inhibition mechanisms. These issues
should be addressed to build effective SNNs for image recognition
Neuromorphic Engineering Editors' Pick 2021
This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors
Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems
Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either due to lack of connectivity or for privacy concerns. However, these circuits are typically noisy and imprecise, because they are affected by device-to-device variability, and operate with extremely small currents. So achieving reliable computation and high accuracy following this approach is still an open challenge that has hampered progress on the one hand and limited widespread adoption of this technology on the other. By construction, these hardware processing systems have many constraints that are biologically plausible, such as heterogeneity and non-negativity of parameters. More and more evidence is showing that applying such constraints to artificial neural networks, including those used in artificial intelligence, promotes robustness in learning and improves their reliability. Here we delve even more into neuroscience and present network-level brain-inspired strategies that further improve reliability and robustness in these neuromorphic systems: we quantify, with chip measurements, to what extent population averaging is effective in reducing variability in neural responses, we demonstrate experimentally how the neural coding strategies of cortical models allow silicon neurons to produce reliable signal representations, and show how to robustly implement essential computational primitives, such as selective amplification, signal restoration, working memory, and relational networks, exploiting such strategies. We argue that these strategies can be instrumental for guiding the design of robust and reliable ultra-low power electronic neural processing systems implemented using noisy and imprecise computing substrates such as subthreshold neuromorphic circuits and emerging memory technologies
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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