Add to ORKGIn order to benefit from the advantages of localist coding, neural models that feature winner-take-all representations at the top level of a network hierarchy must still solve the computational problems inherent in distributed representations at the lower levels
Localist networks represent information in a very simple and straightforward way. However, localist ...
The way information is represented and processed in a neural network may have important consequences...
The Locally Competitive Algorithm (LCA) is a recurrent neural network for performing sparse coding a...
In order to benefit from the advantages of localist coding, neural models that feature winner-take-a...
Over the last decade, fully distributed models have become dominant in connectionist psychological m...
Over the last decade, fully distributed models have become dominant in connectionist psychological m...
The question of how information can be represented in networks of neurons is central to cognitive sc...
The question of how information can be represented in networks of neurons is central to cognitive sc...
A dynamic threshold, which controls the nature and course of learning, is a pivotal concept in Page'...
A long running debate has concerned the question of whether neural representations are encoded using...
Connectionism is the theory that sees brain in terms of neural or parallel distributed processing ne...
In this article, I present the theory that localist representation is used widely in the brain start...
This is an Accepted Manuscript of an article published by Taylor & Francis in Language, Cognition an...
Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2...
Local learning neural networks have long been limited by their inability to store correlated pattern...
Localist networks represent information in a very simple and straightforward way. However, localist ...
The way information is represented and processed in a neural network may have important consequences...
The Locally Competitive Algorithm (LCA) is a recurrent neural network for performing sparse coding a...
In order to benefit from the advantages of localist coding, neural models that feature winner-take-a...
Over the last decade, fully distributed models have become dominant in connectionist psychological m...
Over the last decade, fully distributed models have become dominant in connectionist psychological m...
The question of how information can be represented in networks of neurons is central to cognitive sc...
The question of how information can be represented in networks of neurons is central to cognitive sc...
A dynamic threshold, which controls the nature and course of learning, is a pivotal concept in Page'...
A long running debate has concerned the question of whether neural representations are encoded using...
Connectionism is the theory that sees brain in terms of neural or parallel distributed processing ne...
In this article, I present the theory that localist representation is used widely in the brain start...
This is an Accepted Manuscript of an article published by Taylor & Francis in Language, Cognition an...
Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2...
Local learning neural networks have long been limited by their inability to store correlated pattern...
Localist networks represent information in a very simple and straightforward way. However, localist ...
The way information is represented and processed in a neural network may have important consequences...
The Locally Competitive Algorithm (LCA) is a recurrent neural network for performing sparse coding a...