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Literature DB >> 7353062

On associative memory.

Abstract

The information storing capacity of certain associative and auto-associative memories is calculated. For example, in a 100 X 100 matrix of 1 bit storage elements more than 6,500 bits can be stored associatively, and more than 688,000 bits in a 1,000 X 1,000 matrix. Asymptotically, the storage capacity of an associative memory increases proportionally to the number of storage elements. The usefulness of associative memories, as opposed to conventional listing memories, is duscussed -- especially in connection with brain modelling.

Mesh：

Year: 1980 PMID： 7353062 DOI： 10.1007/bf00337019

Source DB: PubMed Journal: Biol Cybern ISSN： 0340-1200 Impact factor: 2.086

14 in total

1. On optimal nonlinear associative recall.

Authors: T Poggio
Journal: Biol Cybern Date: 1975-09-18 Impact factor: 2.086

2. Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements.

Authors: T Kohonen; E Oja
Journal: Biol Cybern Date: 1976-01-08 Impact factor: 2.086

2. A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields.

Authors: Martin Rehn; Friedrich T Sommer
Journal: J Comput Neurosci Date: 2007-04 Impact factor: 1.621

On associative memory.

1. On optimal nonlinear associative recall.

2. Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements.

3. A theory for the development of feature detecting cells in visual cortex.

4. [Capabilities of an associative storage system compared with the function of the brain (author's transl)].

5. Simple memory: a theory for archicortex.

6. A theory for cerebral neocortex.

7. Models for the brain.

8. Non-holographic associative memory.

9. A theory of cerebellar cortex.

10. A memory storage model utilizing spatial correlation functions.

1. Rate coherence and event coherence in the visual cortex: a neuronal model of object recognition.

2. A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields.

3. Cross-talk theory of memory capacity in neural networks.

4. Sequential configuration model for firing patterns in local neural networks.

5. Forming sparse representations by local anti-Hebbian learning.

6. Associative recognition and storage in a model network of physiological neurons.

7. Cooperative effects of neuronal ensembles.

8. Parallel processing for associative and neuronal networks.

9. Neural networks and physical systems with emergent collective computational abilities.

10. Towards a theory of cell assemblies.