scholarly journals A Systematic Method for Configuring VLSI Networks of Spiking Neurons

2011 ◽  
Vol 23 (10) ◽  
pp. 2457-2497 ◽  
Author(s):  
Emre Neftci ◽  
Elisabetta Chicca ◽  
Giacomo Indiveri ◽  
Rodney Douglas
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Temporal Dynamics ◽  
General Purpose ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Mapping Technique ◽  
Seamless Integration ◽  
High Level ◽  
Scale Integration

An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e.g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.

scholarly journals A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

2015 ◽  
Vol 9 ◽  
Author(s):  
Runchun M. Wang ◽  
Tara J. Hamilton ◽  
Jonathan C. Tapson ◽  
André van Schaik
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Large Scale ◽  
Spike Timing

An Event-Based Neural Network Architecture with Content Addressable Memory

2020 ◽  
Vol 11 (1) ◽  
pp. 23-40 ◽  
Author(s):  
Sivaganesan S ◽  
Maria Antony S ◽  
Udayakumar E
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Synaptic Weight ◽  
Current Mode ◽  
Spike Timing ◽  
Event Representation ◽  
Content Addressable Memory ◽  
Neural Computations ◽  
Scale Integration

A hybrid analog/digital very large-scale integration (VLSI) implementation of a spiking neural network with programmable synaptic weights was designed. The synaptic weight values are stored in an asynchronous module, which is interfaced to a fast current-mode event-driven DAC for producing synaptic currents with the appropriate amplitude values. It acts as a transceiver, receiving asynchronous events for input, performing neural computations with hybrid analog/digital circuits on the input spikes, and eventually producing digital asynchronous events in output. Input, output, and synaptic weight values are transmitted to/from the chip using a common communication protocol based on the address event representation (AER). Using this representation, it is possible to interface the device to a workstation or a microcontroller and explore the effect of different types of spike-timing dependent plasticity (STDP) learning algorithms for updating the synaptic weights values in the CAM module.

scholarly journals LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2123 ◽  
Author(s):  
Lingfei Mo ◽  
Minghao Wang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Structure ◽  
Large Scale ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Logical Operation ◽  
Large Scale Network ◽  
Learning Capabilities ◽  
Scale Network

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.

scholarly journals PhosPiR: An automated phospho-proteomic pipeline in R

2021 ◽  
Author(s):  
Ye Hong ◽  
Dani Flinkman ◽  
Tomi Suomi ◽  
Sami Pietilä ◽  
Peter James ◽  
...  
Keyword(s):  
Large Scale ◽  
Differential Expression Analysis ◽  
Analysis Data ◽  
Statistical Testing ◽  
Pathophysiological Mechanism ◽  
Seamless Integration ◽  
Knowledge Resources ◽  
Data Output ◽  
Programming Knowledge ◽  
High Level

ABSTRACTLarge-scale phospho-proteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from this data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from largescale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phospho-site annotation and translation across species, multi-level enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.

Gas path fault diagnosis framework for a three-shaft gas turbine

2003 ◽  
Vol 217 (2) ◽  
pp. 149-157 ◽  
Author(s):  
S. O T. Ogaji ◽  
R Singh
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Large Scale ◽  
Sensor Faults ◽  
Large Scale Integration ◽  
Multiple Sensor ◽  
Multiple Component ◽  
Gas Path Fault ◽  
Diagnostic Framework ◽  
Scale Integration

A diagnostic framework has been developed for the detection of faults in the gas path of a three-shaft aeroderivative gas turbine thermodynamically similar to the Rolls Royce RB211-24GT. The framework involves a large-scale integration of artificial neural networks (ANNs) designed and trained to detect, isolate and assess faults in the gas path components of the engine. The approach has the capacity to assess both multiple-component and multiple-sensor faults. The results obtained demonstrate the promise of ANNs applied to engine diagnostic activities.

The influence of VLSI technology on computer architecture

1988 ◽  
Vol 326 (1591) ◽  
pp. 377-393 ◽  
Keyword(s):  
Computer Architecture ◽  
Programming Languages ◽  
Large Scale ◽  
Parallel Computers ◽  
General Purpose ◽  
Parallel Computer ◽  
Vlsi Technology ◽  
Large Scale Integration ◽  
Scale Integration ◽  
The Cost

Very-large-scale integration (VLSI) offers new opportunities in computer architecture. The cost of a processor has been reduced to that of a few thousand bytes of memory, with the result that parallel computers can be constructed as easily and economically as their sequential predecessors. In particular, a parallel computer constructed by replication of a standard computing element is well suited to the mass-production economics of the technology. The emergence of the new parallel computers has stimulated the development of new programming languages and algorithms. One example is the Occam language which has been designed to enable applications to be expressed in a form suitable for execution on a variety of parallel architectures. Further developments in language and architecture will enable processing resources to be allocated and deallocated as freely as memory, giving rise to some hope that users of general-purpose parallel computers will be freed from the current need to design algorithms to suit specific architectures.

scholarly journals Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System

2021 ◽  
Vol 15 ◽  
Author(s):  
Abderazek Ben Abdallah ◽  
Khanh N. Dang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Three Dimensional ◽  
Network On Chip ◽  
Spike Timing ◽  
Dimensional Structure ◽  
Spiking Neural Network ◽  
3D Ics ◽  
On Chip

Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation.

scholarly journals Applying stochastic spike train theory for high-accuracy MEG/EEG

2019 ◽  
Author(s):  
Niels Trusbak Haumann ◽  
Minna Huotilainen ◽  
Peter Vuust ◽  
Elvira Brattico
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Brain Activity ◽  
Component Analysis ◽  
High Accuracy ◽  
Spike Timing ◽  
Clinical Diagnostics ◽  
Evoked Response ◽  
Brain Responses ◽  
The Brain

AbstractThe accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) is challenged by overlapping sources from within the brain. This lack of accuracy is a severe limitation to the possibilities and reliability of modern stimulation protocols in basic research and clinical diagnostics. As a solution, we here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural networks, and a novel spike density component analysis (SCA) method for isolating specific neural sources. Three studies are conducted based on 564 cases of evoked responses to auditory stimuli from 94 human subjects each measured with 60 EEG electrodes and 306 MEG sensors. In the first study we show that the large-scale spike timing (but not non-encephalographic artifacts) in MEG/EEG waveforms can be modeled with Gaussian probability density functions with high accuracy (median 99.7%-99.9% variance explained), while gamma and sine functions fail describing the MEG and EEG waveforms. In the second study we confirm that SCA can isolate a specific evoked response of interest. Our findings indicate that the mismatch negativity (MMN) response is accurately isolated with SCA, while principal component analysis (PCA) fails supressing interference from overlapping brain activity, e.g. from P3a and alpha waves, and independent component analysis (ICA) distorts the evoked response. Finally, we confirm that SCA accurately reveals inter-individual variation in evoked brain responses, by replicating findings relating individual traits with MMN variations. The findings of this paper suggest that the commonly overlapping neural sources in single-subject or patient data can be more accurately separated by applying the introduced theory of large-scale spike timing and method of SCA in comparison to PCA and ICA.Significance statementElectroencephalography (EEG) and magnetoencelopraphy (MEG) are among the most applied non-invasive brain recording methods in humans. They are the only methods to measure brain function directly and in time resolutions smaller than seconds. However, in modern research and clinical diagnostics the brain responses of interest cannot be isolated, because of interfering signals of other ongoing brain activity. For the first time, we introduce a theory and method for mathematically describing and isolating overlapping brain signals, which are based on prior intracranial in vivo research on brain cells in monkey and human neural networks. Three studies mutually support our theory and suggest that a new level of accuracy in MEG/EEG can achieved by applying the procedures presented in this paper.

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