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

Gilson et al., 2009 - Google Patents

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity—symmetry breaking

Gilson et al., 2009

View PDF
Document ID
17392770672489075940
Author
Gilson M
Burkitt A
Grayden D
Thomas D
Van Hemmen J
Publication year
Publication venue
Biological Cybernetics

External Links

Snippet

Spike-timing-dependent plasticity (STDP) is believed to structure neuronal networks by slowly changing the strengths (or weights) of the synaptic connections between neurons depending upon their spiking activity, which in turn modifies the neuronal firing dynamics. In …
Continue reading at sites.me.ucsb.edu (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
    • G06N3/08—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
    • G06N3/08—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
    • G06N3/04—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
    • G06N3/12—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
    • 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
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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

Similar Documents

Publication Publication Date Title
Gilson et al. Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways
Gilson et al. Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity—symmetry breaking
US8504502B2 (en) Prediction by single neurons
US9558442B2 (en) Monitoring neural networks with shadow networks
KR20170031695A (en) Decomposing convolution operation in neural networks
Carlson et al. Biologically plausible models of homeostasis and STDP: stability and learning in spiking neural networks
KR101700145B1 (en) Automated method for modifying neural dynamics
KR20160138002A (en) Training, recognition, and generation in a spiking deep belief network (dbn)
KR20160076520A (en) Causal saliency time inference
TW201531966A (en) Configuring sparse neuronal networks
TW201541374A (en) Event-based inference and learning for random spike Bayesian networks
US8423490B2 (en) Method for computer-aided learning of a neural network and neural network
KR20160076531A (en) Evaluation of a system including separable sub-systems over a multidimensional range
Bakhshiev et al. Mathematical Model of the Impulses Transformation Processes in Natural Neurons for Biologically Inspired Control Systems Development.
Hertz et al. Learning short synfire chains by self-organization
KR20160068823A (en) Congestion avoidance in networks of spiking neurons
Gilson et al. Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks III: Partially connected neurons driven by spontaneous activity
Gilson et al. Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence
Ramanathan et al. Presynaptic learning and memory with a persistent firing neuron and a habituating synapse: A model of short term persistent habituation
Rao et al. Self–organizing neural systems based on predictive learning
Bohte et al. Reducing the variability of neural responses: A computational theory of spike-timing-dependent plasticity
Liu Learning rule of homeostatic synaptic scaling: Presynaptic dependent or not
Chang et al. Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm
KR101122158B1 (en) Prediction by single neurons and networks
Ellefsen Evolved sensitive periods in learning