[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Nouri et al., 2015 - Google Patents

Digital multiplierless implementation of the biological FitzHugh–Nagumo model

Nouri et al., 2015

View PDF
Document ID
4453426149730024158
Author
Nouri M
Karimi G
Ahmadi A
Abbott D
Publication year
Publication venue
Neurocomputing

External Links

Snippet

High-accuracy implementation of biological neural networks (NN) is a task with high computational overheads, especially in the case of large-scale realizations of neuromorphic algorithms. This paper presents a set of piecewise linear FitzHugh–Nagumo (FHN) models …
Continue reading at www.eleceng.adelaide.edu.au (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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation
    • G06F7/48—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state device; using unspecified devices
    • G06F7/52—Multiplying; Dividing
    • G06F7/523—Multiplying only
    • G06F7/53—Multiplying only in parallel-parallel fashion, i.e. both operands being entered in parallel
    • 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
    • G06F17/5036—Computer-aided design using simulation for analog modelling, e.g. for circuits, spice programme, direct methods, relaxation methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation
    • G06F7/48—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state device; using unspecified devices
    • G06F7/544—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state device; using unspecified devices for evaluating functions by calculation
    • 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
    • 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
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/13—Differential equations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2207/00—Indexing scheme relating to methods or arrangements for processing data by operating upon the order or content of the data handled

Similar Documents

Publication Publication Date Title
Nouri et al. Digital multiplierless implementation of the biological FitzHugh–Nagumo model
Wang et al. Energy efficient parallel neuromorphic architectures with approximate arithmetic on FPGA
Farsa et al. A low-cost high-speed neuromorphic hardware based on spiking neural network
Heidarpour et al. A CORDIC based digital hardware for adaptive exponential integrate and fire neuron
Hayati et al. Digital multiplierless realization of two coupled biological Morris-Lecar neuron model
Dahasert et al. Experimental realizations of the HR neuron model with programmable hardware and synchronization applications
Pearson et al. Implementing spiking neural networks for real-time signal-processing and control applications: A model-validated FPGA approach
Haghiri et al. Multiplierless implementation of noisy Izhikevich neuron with low-cost digital design
Soleimani et al. Digital implementation of a biological astrocyte model and its application
Heidarpur et al. A digital implementation of 2D Hindmarsh–Rose neuron
Hayati et al. A digital realization of astrocyte and neural glial interactions
Hayati et al. Digital multiplierless realization of two-coupled biological Hindmarsh–Rose neuron model
Haghiri et al. High speed and low digital resources implementation of Hodgkin-Huxley neuronal model using base-2 functions
Zahedi et al. Multiplierless digital implementation of time-varying FitzHugh–Nagumo model
Alomar et al. Digital implementation of a single dynamical node reservoir computer
Chen et al. A real-time FPGA implementation of a biologically inspired central pattern generator network
Li et al. An FPGA-based silicon neuronal network with selectable excitability silicon neurons
Jokar et al. An efficient uniform-segmented neuron model for large-scale neuromorphic circuit design: Simulation and FPGA synthesis results
Jokar et al. A novel nonlinear function evaluation approach for efficient FPGA mapping of neuron and synaptic plasticity models
Chen et al. Analog/digital circuit simplification for Hopfield neural network
Rahimian et al. Digital implementation of the two-compartmental Pinsky–Rinzel pyramidal neuron model
Haghiri et al. A novel digital realization of AdEx neuron model
Nambiar et al. Hardware implementation of evolvable block-based neural networks utilizing a cost efficient sigmoid-like activation function
Ghiasi et al. Field-programmable gate arrays-based Morris-Lecar implementation using multiplierless digital approach and new divider-exponential modules
Magerl et al. Echo state networks for black-box modeling of integrated circuits