Abstract:
We introduce a methodology for constructing an associative memory that is highly robust in the presence of noisy inputs. The memory is based on dendritic computing employ...Show MoreMetadata
Abstract:
We introduce a methodology for constructing an associative memory that is highly robust in the presence of noisy inputs. The memory is based on dendritic computing employing lattice algebraic operations. A major consequence of this approach is the avoidance of convergence problems during the training phase and rapid association of perfect and nonperfect input patterns with stored associated patterns.
Date of Conference: 31 July 2011 - 05 August 2011
Date Added to IEEE Xplore: 03 October 2011
ISBN Information:
ISSN Information:
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- IEEE Keywords
- Index Terms
- Input Patterns ,
- Noisy Input ,
- Perfect Recall ,
- Neural Network ,
- Support Vector Machine ,
- Artificial Neural Network ,
- Hidden Layer ,
- Total Yield ,
- Dimensional Vector ,
- Neurons In Layer ,
- Grayscale Images ,
- Presence Of Noise ,
- Training Algorithm ,
- Neurons In The Hidden Layer ,
- Hidden Neurons ,
- Dendritic Branching ,
- Synaptic Weights ,
- Axon Branching ,
- Axonal Fibers ,
- Postsynaptic Responses ,
- Exemplary Images ,
- Normalized Mean Square Error ,
- Straight Forward ,
- Noise Parameters ,
- Multilayer Perceptron ,
- Large Amount Of Noise ,
- Input Layer ,
- Output Layer ,
- Hyperplane ,
- Output Neurons
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Input Patterns ,
- Noisy Input ,
- Perfect Recall ,
- Neural Network ,
- Support Vector Machine ,
- Artificial Neural Network ,
- Hidden Layer ,
- Total Yield ,
- Dimensional Vector ,
- Neurons In Layer ,
- Grayscale Images ,
- Presence Of Noise ,
- Training Algorithm ,
- Neurons In The Hidden Layer ,
- Hidden Neurons ,
- Dendritic Branching ,
- Synaptic Weights ,
- Axon Branching ,
- Axonal Fibers ,
- Postsynaptic Responses ,
- Exemplary Images ,
- Normalized Mean Square Error ,
- Straight Forward ,
- Noise Parameters ,
- Multilayer Perceptron ,
- Large Amount Of Noise ,
- Input Layer ,
- Output Layer ,
- Hyperplane ,
- Output Neurons