154 research outputs found
Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition
Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology. Remarkable progress has been made recently, and complex artificial neural sensory-motor systems can be built using this technology. Today, however, NE stands before a large conceptual challenge that must be met before there will be significant progress toward an age of genuinely intelligent neuromorphic machines. The challenge is to bridge the gap from reactive systems to ones that are cognitive in quality. In this paper, we describe recent advancements in NE, and present examples of neuromorphic circuits that can be used as tools to address this challenge. Specifically, we show how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilitie
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been
demonstrated to perform efficiently in a variety of applications, such as
dimensionality reduction, feature learning, and classification. Their
implementation on neuromorphic hardware platforms emulating large-scale
networks of spiking neurons can have significant advantages from the
perspectives of scalability, power dissipation and real-time interfacing with
the environment. However the traditional RBM architecture and the commonly used
training algorithm known as Contrastive Divergence (CD) are based on discrete
updates and exact arithmetics which do not directly map onto a dynamical neural
substrate. Here, we present an event-driven variation of CD to train a RBM
constructed with Integrate & Fire (I&F) neurons, that is constrained by the
limitations of existing and near future neuromorphic hardware platforms. Our
strategy is based on neural sampling, which allows us to synthesize a spiking
neural network that samples from a target Boltzmann distribution. The recurrent
activity of the network replaces the discrete steps of the CD algorithm, while
Spike Time Dependent Plasticity (STDP) carries out the weight updates in an
online, asynchronous fashion. We demonstrate our approach by training an RBM
composed of leaky I&F neurons with STDP synapses to learn a generative model of
the MNIST hand-written digit dataset, and by testing it in recognition,
generation and cue integration tasks. Our results contribute to a machine
learning-driven approach for synthesizing networks of spiking neurons capable
of carrying out practical, high-level functionality.Comment: (Under review
Synthesizing cognition in neuromorphic electronic systems
The quest to implement intelligent processing in electronic neuromorphic systems lacks methods for achieving reliable behavioral dynamics on substrates of inherently imprecise and noisy neurons. Here we report a solution to this problem that involves first mapping an unreliable hardware layer of spiking silicon neurons into an abstract computational layer composed of generic reliable subnetworks of model neurons and then composing the target behavioral dynamics as a âsoft state machineâ running on these reliable subnets. In the first step, the neural networks of the abstract layer are realized on the hardware substrate by mapping the neuron circuit bias voltages to the model parameters. This mapping is obtained by an automatic method in which the electronic circuit biases are calibrated against the model parameters by a series of population activity measurements. The abstract computational layer is formed by configuring neural networks as generic soft winner-take-all subnetworks that provide reliable processing by virtue of their active gain, signal restoration, and multistability. The necessary states and transitions of the desired high-level behavior are then easily embedded in the computational layer by introducing only sparse connections between some neurons of the various subnets. We demonstrate this synthesis method for a neuromorphic sensory agent that performs real-time context-dependent classification of motion patterns observed by a silicon retina
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 EconomĂa y Competitividad TEC2012-37868-C04-02Junta de AndalucĂa P12-TIC-130
Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks
In the rapid evolution of next-generation brain-inspired artificial
intelligence and increasingly sophisticated electromagnetic environment, the
most bionic characteristics and anti-interference performance of spiking neural
networks show great potential in terms of computational speed, real-time
information processing, and spatio-temporal information processing. Data
processing. Spiking neural network is one of the cores of brain-like artificial
intelligence, which realizes brain-like computing by simulating the structure
and information transfer mode of biological neural networks. This paper
summarizes the strengths, weaknesses and applicability of five neuronal models
and analyzes the characteristics of five network topologies; then reviews the
spiking neural network algorithms and summarizes the unsupervised learning
algorithms based on synaptic plasticity rules and four types of supervised
learning algorithms from the perspectives of unsupervised learning and
supervised learning; finally focuses on the review of brain-like neuromorphic
chips under research at home and abroad. This paper is intended to provide
learning concepts and research orientations for the peers who are new to the
research field of spiking neural networks through systematic summaries
2022 roadmap on neuromorphic computing and engineering
Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community
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