Yucedag et al., 2023 - Google Patents
Raspberry Pi implementation of the Wilson-Cowan neural network with chemical synapseYucedag et al., 2023
- Document ID
- 689221937270506862
- Author
- Yucedag V
- Dalkiran I
- Publication year
- Publication venue
- 2023 Innovations in Intelligent Systems and Applications Conference (ASYU)
External Links
Snippet
Information transmission in living things occurs through synapse connections between neurons and the transfer of ions from one neuron to another. Synapses can be Electrical and Chemical. Transmission at electrical synapses is direct and very fast. Unlike chemical …
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
- G06N3/08—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
- G06N3/0454—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
- G06N3/10—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
- G06N3/12—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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chang et al. | Parameter optimization in models of the olfactory neural system | |
| Izhikevich | Which model to use for cortical spiking neurons? | |
| Laing et al. | Stochastic methods in neuroscience | |
| US7174325B1 (en) | Neural processor | |
| Dahmen et al. | Correlated fluctuations in strongly coupled binary networks beyond equilibrium | |
| Hu et al. | Synchronization of scale-free neuronal network with small-world property induced by spike-timing-dependent plasticity under time delay | |
| Li et al. | Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity | |
| Kuznetsov et al. | An asynchronous discrete model of chemical interactions in simple neuronal systems | |
| Wang et al. | Active processing of spatio-temporal input patterns in silicon dendrites | |
| Ponulak | Supervised learning in spiking neural networks with ReSuMe method | |
| Yu et al. | Emergence of phase clusters and coexisting states reveals the structure-function relationship | |
| Liu | Learning rule of homeostatic synaptic scaling: Presynaptic dependent or not | |
| Yucedag et al. | Raspberry Pi implementation of the Wilson-Cowan neural network with chemical synapse | |
| Antonopoulos et al. | Evaluating performance of neural codes in model neural communication networks | |
| Ranhel et al. | Bistable memory and binary counters in spiking neural network | |
| Azghadi et al. | Design and implementation of BCM rule based on spike-timing dependent plasticity | |
| Marghoti et al. | Coupling dependence on chaos synchronization process in a network of rulkov neurons | |
| Eskandari et al. | Effect of spike-timing-dependent plasticity on neural assembly computing | |
| Gürcan | Effective connectivity at synaptic level in humans: a review and future prospects | |
| Mes et al. | Neuromorphic self-organizing map design for classification of bioelectric-timescale signals | |
| Wang et al. | A biological plausible generalized leaky integrate-and-fire neuron model | |
| Deng et al. | The implementation of feedforward network on field programmable gate array | |
| Belavkin et al. | Conflict resolution and learning probability matching in a neural cell-assembly architecture | |
| Tan et al. | Interaction of neuronal and network mechanisms on firing propagation in a feedforward network | |
| de Oliveira-Neto et al. | Magnitude comparison in analog spiking neural assemblies |