Hertz et al., 1996 - Google Patents
Learning short synfire chains by self-organizationHertz et al., 1996
View PDF- Document ID
- 5913363387279260695
- Author
- Hertz J
- Prügel-Bennett A
- Publication year
- Publication venue
- Network: Computation in Neural Systems
External Links
Snippet
A model of cortical neurons capable of sustaining a low level of spontaneous activity is investigated. Without learning the activity of the network is chaotic. We report on attempts to learn synfire chains in this type of network by introducing a Hebbian learning mechanism …
- 230000000694 effects 0 abstract description 52
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
- G06N3/08—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
- G06N3/06—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
- 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
- G06N3/08—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
- G06N3/12—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
- 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
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/02—Computer systems based on specific mathematical models using fuzzy logic
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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 |
---|---|---|
Hertz et al. | Learning short synfire chains by self-organization | |
Rubin et al. | Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity | |
Vazquez | Training spiking neural models using cuckoo search algorithm | |
Griniasty et al. | Conversion of temporal correlations between stimuli to spatial correlations between attractors | |
Song et al. | Competitive Hebbian learning through spike-timing-dependent synaptic plasticity | |
Herrmann et al. | Analysis of synfire chains | |
US7080053B2 (en) | Neural network device for evolving appropriate connections | |
Bengio et al. | STDP as presynaptic activity times rate of change of postsynaptic activity | |
Gilson et al. | Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity—symmetry breaking | |
Hosaka et al. | STDP provides the substrate for igniting synfire chains by spatiotemporal input patterns | |
Treves et al. | Low firing rates: an effective Hamiltonian for excitatory neurons | |
Fusi et al. | Neurophysiology of a VLSI spiking neural network: LANN21 | |
Sala et al. | Self-organization in networks of spiking neurons | |
Roberts | Dynamics of temporal learning rules | |
Ellefsen | Evolved sensitive periods in learning | |
Wang | Noise injection into inputs in sparsely connected Hopfield and winner-take-all neural networks | |
Metzger et al. | Learning temporal sequences by local synaptic changes | |
Shen et al. | Oscillations and spiking pairs: behavior of a neuronal model with STDP learning | |
Viswanathan et al. | A Study of Prefrontal Cortex Task Switching Using Spiking Neural Networks | |
Lu et al. | Impact of network topology on decision-making | |
Fournou et al. | A Gaussian approach to neural nets with multiple memory domains | |
Metzger et al. | Learning temporal sequences by excitatory synaptic changes only | |
Reyes et al. | Analysis of synfire chains above saturation | |
Wu et al. | Enhancing the performance of a hippocampal model by increasing variability early in learning | |
Flanagan | Self-organized criticality and the self-organizing map |