[go: up one dir, main page]

Maass, 1997 - Google Patents

On the relevance of time in neural computation and learning

Maass, 1997

View PS
Document ID
3366386347249957995
Author
Maass W
Publication year
Publication venue
International Workshop on Algorithmic Learning Theory

External Links

Snippet

We discuss models for computation in biological neural systems that are based on the current state of knowledge in neurophysiology. Differences and similarities to traditional neural network models are highlighted. It turns out that many important questions regarding …
Continue reading at igi-web.tugraz.at (PS) (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
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • 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
    • G06N3/049—Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
    • 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
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • 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
    • G06N3/0454—Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • 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
    • G06N3/067—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical 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
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
    • 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
    • G06N3/10—Simulation on general purpose computers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/004—Artificial life, i.e. computers simulating life
    • G06N3/008—Artificial life, i.e. computers simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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

Similar Documents

Publication Publication Date Title
Sporea et al. Supervised learning in multilayer spiking neural networks
Maass Networks of spiking neurons: the third generation of neural network models
Siegelmann Neural and super-Turing computing
US6643627B2 (en) Dynamic synapse for signal processing in neural networks
US5255348A (en) Neural network for learning, recognition and recall of pattern sequences
Teo et al. Wavelet packet multi-layer perceptron for chaotic time series prediction: effects of weight initialization
Belatreche et al. Advances in design and application of spiking neural networks
US5666518A (en) Pattern recognition by simulated neural-like networks
Maass On the relevance of time in neural computation and learning
Maass Paradigms for computing with spiking neurons
Liaw et al. Dynamic synapse: Harnessing the computing power of synaptic dynamics
Maass On the relevance of time in neural computation and learning
US5313558A (en) System for spatial and temporal pattern learning and recognition
Peterson et al. Modulating STDP with back-propagated error signals to train SNNs for audio classification
Eriksson et al. Spiking neural networks for reconfigurable POEtic tissue
Nowotny et al. Spatial representation of temporal information through spike-timing-dependent plasticity
Doya et al. Dimension reduction of biological neuron models by artificial neural networks
Maass Computation and Learning
Maass A simple model for neural computation with firing rates and firing correlations
Zamani et al. A bidirectional associative memory based on cortical spiking neurons using temporal coding
Valko et al. Evolutionary feature selection for spiking neural network pattern classifiers
es PEREZ-URIBE Artificial neural networks: Algorithms and hardware implementation
Gurney Weighted nodes and RAM-nets: A unified approach
El-Laithy et al. Synchrony state generation in artificial neural networks with stochastic synapses
Ruf Computing functions with spiking neurons in temporal coding