[go: up one dir, main page]

Galluppi et al., 2012 - Google Patents

Real time on-chip implementation of dynamical systems with spiking neurons

Galluppi et al., 2012

View PDF
Document ID
8742903608730786138
Author
Galluppi F
Davies S
Furber S
Stewart T
Eliasmith C
Publication year
Publication venue
The 2012 International Joint Conference on Neural Networks (IJCNN)

External Links

Snippet

Simulation of large-scale networks of spiking neurons has become appealing for understanding the computational principles of the nervous system by producing models based on biological evidence. In particular, networks that can assume a variety of …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications

Similar Documents

Publication Publication Date Title
Davies et al. Advancing neuromorphic computing with loihi: A survey of results and outlook
Galluppi et al. Real time on-chip implementation of dynamical systems with spiking neurons
Lechner et al. Learning long-term dependencies in irregularly-sampled time series
US8990133B1 (en) Apparatus and methods for state-dependent learning in spiking neuron networks
Chicca et al. Neuromorphic electronic circuits for building autonomous cognitive systems
US9256215B2 (en) Apparatus and methods for generalized state-dependent learning in spiking neuron networks
Yassin et al. Binary particle swarm optimization structure selection of nonlinear autoregressive moving average with exogenous inputs (NARMAX) model of a flexible robot arm
WO2019022854A1 (en) Data2data: deep learning for time series representation and retrieval
US20200110999A1 (en) Thermodynamic ram technology stack
WO2014018793A1 (en) Apparatus and methods for efficient updates in spiking neuron networks
Severa et al. Spiking network algorithms for scientific computing
Wei et al. Learning motion rules from real data: Neural network for crowd simulation
Conradt et al. Trainable sensorimotor mapping in a neuromorphic robot
Ranjan et al. A novel and efficient classifier using spiking neural network
Cardwell et al. Truly heterogeneous HPC: Co-design to achieve what science needs from HPC
Lozhkin et al. Convolutional neural networks training for autonomous robotics
WO2025062034A1 (en) Feature extraction and encoding of spiking neural networks using convolutional neural network and trainable encoders for deployment in neuromorphic chips
Zhu Theoretical framework and advanced applications of spiking neural networks
Liu et al. SC-PLR: An approximate spiking neural network accelerator with on-chip predictive learning rule
Son et al. A survey of brain inspired technologies for engineering
Yang et al. Broad learning system for human activity recognition using sensor data
Reiter et al. Neuromorphic processing and sensing: evolutionary progression of AI to spiking
Lechner et al. Mixed-memory rnns for learning long-term dependencies in irregularly sampled time series
Khonina et al. Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review. Nanomaterials 2024, 14, 697
Singhal et al. A Systematic Review of Spiking Neural Networks and Their Applications