Kaushik et al., 2022 - Google Patents
Recurrent neural network: A flexible tool of computational neuroscience researchKaushik et al., 2022
View PDF- Document ID
- 3065970887836846887
- Author
- Kaushik A
- Singh J
- Mahajan S
- Publication year
- Publication venue
- Proceedings of the Third International Conference on Information Management and Machine Intelligence: ICIMMI 2021
External Links
Snippet
Recurrent neural networks (RNNs) are a set of computational models which are repeatedly used as a mechanism to acquire insights into neurological phenomena, by considering computational, electrophysiological and anatomical constraints. RNNs can either be trained …
- 230000001537 neural 0 title abstract description 23
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
- 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
-
- 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
- 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
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Alaloul et al. | Data processing using artificial neural networks | |
Lukoševičius et al. | Reservoir computing trends | |
Tino et al. | Artificial neural network models | |
Khan et al. | A novel fractional gradient-based learning algorithm for recurrent neural networks | |
Panda et al. | How effective is the salp swarm algorithm in data classification | |
Ranjan et al. | A novel and efficient classifier using spiking neural network | |
Ahmadi et al. | Bridging the gap between probabilistic and deterministic models: a simulation study on a variational bayes predictive coding recurrent neural network model | |
Muni Kumar et al. | Design of multi-layer perceptron for the diagnosis of diabetes mellitus using keras in deep learning | |
Kaushik et al. | Recurrent neural network: A flexible tool of computational neuroscience research | |
Ahmad et al. | Confusion matrix-based modularity induction into pretrained CNN | |
Napoli et al. | An object-oriented neural network toolbox based on design patterns | |
Shobana et al. | A recurrent neural network-based identification of complex nonlinear dynamical systems: a novel structure, stability analysis and a comparative study | |
Raijmakers et al. | Modeling developmental transitions in adaptive resonance theory | |
Ben-Bright et al. | Taxonomy and a theoretical model for feedforward neural networks | |
Aspiras et al. | Hierarchical autoassociative polynimial network (hap net) for pattern recognition | |
Brooks et al. | A computer science perspective on models of the mind | |
Galatolo et al. | Using stigmergy to incorporate the time into artificial neural networks | |
Kasabov | Evolving connectionist systems: From Neuro-fuzzy-, to spiking-and Neuro-genetic | |
Opiełka et al. | Application of spiking neural networks to fashion classification | |
Zgurovsky et al. | Formation of Hybrid Artificial Neural Networks Topologies | |
Pedroza et al. | Machine reading comprehension (lstm) review (state of art) | |
Yamada et al. | Dynamical linking of positive and negative sentences to goal-oriented robot behavior by hierarchical rnn | |
Valverde-Albacete et al. | The case for quantifying artificial general intelligence with entropy semifields | |
Sen et al. | Artificial neural network model for forecasting the stock price of Indian IT company | |
Gruodis | Realizations of the Artificial Neural Network for Process Modeling. Overview of Current Implementations |