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Learning of spatiotemporal behaviour in cellular neural networks

https://doi.org/10.1002/cta.346
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cta.191openalex-snapshot
ACE4k: An analog I/O 64×64 visual microprocessor chip with 7‐bit analog accuracy
inferredcites →https://doi.org/10.1016/s0893-6080(96)00072-xopenalex-snapshot
Efficient Training of Recurrent Neural Network with Time Delays
inferredcites →https://doi.org/10.1109/101.526876openalex-snapshot
CNN universal chips crank up the computing power
inferredcites →https://doi.org/10.1109/5.58337openalex-snapshot
Backpropagation through time: what it does and how to do it
inferredcites →https://doi.org/10.1109/72.265960openalex-snapshot
An evolutionary algorithm that constructs recurrent neural networks
inferredcites →https://doi.org/10.1109/72.279181openalex-snapshot
Learning long-term dependencies with gradient descent is difficult
inferredcites →https://doi.org/10.1109/72.279182openalex-snapshot
On the problem of local minima in recurrent neural networks
inferredcites →https://doi.org/10.1109/72.410363openalex-snapshot
Gradient calculations for dynamic recurrent neural networks: a survey
inferredcites →https://doi.org/10.1109/72.668879openalex-snapshot
Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design
inferredcites →https://doi.org/10.1109/72.774210openalex-snapshot
Learning continuous trajectories in recurrent neural networks with time-dependent weights
inferredcites →https://doi.org/10.1109/72.80336openalex-snapshot
Gradient methods for the optimization of dynamical systems containing neural networks
inferredcites →https://doi.org/10.1109/72.991415openalex-snapshot
Training trajectories by continuous recurrent multilayer networks
inferredcites →https://doi.org/10.1109/81.222798openalex-snapshot
Autowaves for image processing on a two-dimensional CNN array of excitable nonlinear circuits: flat and wrinkled labyrinths
inferredcites →https://doi.org/10.1109/81.473590openalex-snapshot
Simulating nonlinear waves and partial differential equations via CNN. I. Basic techniques
inferredcites →https://doi.org/10.1109/81.661681openalex-snapshot
Self-organization in a two-layer CNN
inferredcites →https://doi.org/10.1109/81.747209openalex-snapshot
Fault-tolerant design of analogic CNN templates and algorithms-Part I: The binary output case
inferredcites →https://doi.org/10.1109/tcsi.2004.827618openalex-snapshot
Toward CNN Chip-Specific Robustness
inferredcites →https://doi.org/10.1109/tcsi.2004.827621openalex-snapshot
ACE16k: The Third Generation of Mixed-Signal SIMD-CNN ACE Chips Toward VSoCs
inferredcites →https://doi.org/10.1109/tcsi.2004.827641openalex-snapshot
Second-Order Neural Core for Bioinspired Focal-Plane Dynamic Image Processing in CMOS
inferredcites →https://doi.org/10.1109/tcsi.2004.827654openalex-snapshot
Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors
inferredcites →https://doi.org/10.1109/tnn.2003.810603openalex-snapshot
Trajectory generation and modulation using dynamic neural networks
inferredcites →https://doi.org/10.1142/s0218126603001021openalex-snapshot
COMPUTATIONAL AND COMPUTER COMPLEXITY OF ANALOGIC CELLULAR WAVE COMPUTERS
inferredcites →https://doi.org/10.1142/s0218127406015489openalex-snapshot
SPATIOTEMPORAL PATTERN FORMATION ON THE ACE16k CNN CHIP
inferredcites →https://doi.org/10.1142/s021812749500003xopenalex-snapshot
SPATIOTEMPORAL STRUCTURES IN DISCRETELY-COUPLED ARRAYS OF NONLINEAR CIRCUITS: A REVIEW
inferredcites →https://doi.org/10.1162/089976604773717603openalex-snapshot
Improving Generalization Capabilities of Dynamic Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.cs/0001018openalex-snapshot
Adaptive simulated annealing (ASA): Lessons learned

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cta.433openalex-snapshot
Analysis of nonlinear oscillatory network dynamics via time‐varying amplitude and phase variables
inferred← citeshttps://doi.org/10.1002/cta.437openalex-snapshot
Circuits, computers, and beyond Boolean logic
inferred← citeshttps://doi.org/10.1002/cta.481openalex-snapshot
Single‐layer perceptron and dynamic neuron implementing linearly non‐separable Boolean functions
inferred← citeshttps://doi.org/10.1002/cta.485openalex-snapshot
Cellular wave computer algorithms with spatial semantic embedding for handwritten text recognition
inferred← citeshttps://doi.org/10.1002/cta.504openalex-snapshot
Awakening dynamics via passive coupling and synchronization mechanism in oscillatory cellular neural/nonlinear networks
inferred← citeshttps://doi.org/10.1002/cta.513openalex-snapshot
Toward an autonomous platform for spatio‐temporal EEG‐signal analysis based on cellular nonlinear networks
inferred← citeshttps://doi.org/10.1002/cta.523openalex-snapshot
Global robust stability of interval delayed neural networks: Modified approach
inferred← citeshttps://doi.org/10.1002/cta.567openalex-snapshot
The winnerless competition paradigm in cellular nonlinear networks: Models and applications
inferred← citeshttps://doi.org/10.1002/cta.v37:10openalex-snapshot
inferred← citeshttps://doi.org/10.1007/978-1-4419-1011-0_2openalex-snapshot
Cellular Wave Computing in Nanoscale via Million Processor Chips
inferred← citeshttps://doi.org/10.1109/cnna.2006.341614openalex-snapshot
CNNOPT: Learning dynamics and CNN chip-specific robustness
inferred← citeshttps://doi.org/10.1109/cnna.2010.5430312openalex-snapshot
Pattern formation in oscillatory media: Beyond reaction-diffusion model
inferred← citeshttps://doi.org/10.1109/iscas.2017.8050869openalex-snapshot
Image classification by cellular nonlinear networks
inferred← citeshttps://doi.org/10.1109/tcsii.2008.2002569openalex-snapshot
A Simple Programmable Autowave Generator Network for Wave Computing Applications
inferred← citeshttps://doi.org/10.1142/9789814434805_0005openalex-snapshot
CONTRIBUTIONS OF CNN TO BIO-ROBOTICS AND BRAIN SCIENCE
inferred← citeshttps://doi.org/10.4218/etrij.12.0112.0107openalex-snapshot
Modular Cellular Neural Network Structure for Wave-Computing-Based Image Processing
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