### Abstract

The retrieval process of an associative memory with a general input-output function is studied by means of a signal-to-noise ratio analysis. We derive a set of recursion relations for macroscopic variables to describe the time development of the network. By taking the equilibrium limit of the recursion relations, we find that a certain type of nonmonotonic input-output relation of a single neuron yields an enhanced memory capacity of the network compared with the conventional monotonic relation. This behavior is in agreement with the prediction of Morita et al. who used numerical simulations as well as geometrical arguments to reach their conclusion. Our method reveals the relation between the type of the input-output function and the memory capacity in a generic associative memory.

Original language | English (US) |
---|---|

Pages (from-to) | 1061-1067 |

Number of pages | 7 |

Journal | Neural Networks |

Volume | 6 |

Issue number | 8 |

DOIs | |

State | Published - Jan 1 1993 |

Externally published | Yes |

### Fingerprint

### Keywords

- Amari-Maginu theory
- Associative memory
- Gaussian noise
- Input-output function
- Macrovariables
- Monte Carlo simulation
- Retrieval dynamics
- Signal-to-noise ratio analysis

### ASJC Scopus subject areas

- Cognitive Neuroscience
- Artificial Intelligence

### Cite this

*Neural Networks*,

*6*(8), 1061-1067. https://doi.org/10.1016/S0893-6080(09)80017-8

**Retrieval process of an associative memory with a general input-output function.** / Nishimori, Hidetoshi; Opris, Ioan.

Research output: Contribution to journal › Article

*Neural Networks*, vol. 6, no. 8, pp. 1061-1067. https://doi.org/10.1016/S0893-6080(09)80017-8

}

TY - JOUR

T1 - Retrieval process of an associative memory with a general input-output function

AU - Nishimori, Hidetoshi

AU - Opris, Ioan

PY - 1993/1/1

Y1 - 1993/1/1

N2 - The retrieval process of an associative memory with a general input-output function is studied by means of a signal-to-noise ratio analysis. We derive a set of recursion relations for macroscopic variables to describe the time development of the network. By taking the equilibrium limit of the recursion relations, we find that a certain type of nonmonotonic input-output relation of a single neuron yields an enhanced memory capacity of the network compared with the conventional monotonic relation. This behavior is in agreement with the prediction of Morita et al. who used numerical simulations as well as geometrical arguments to reach their conclusion. Our method reveals the relation between the type of the input-output function and the memory capacity in a generic associative memory.

AB - The retrieval process of an associative memory with a general input-output function is studied by means of a signal-to-noise ratio analysis. We derive a set of recursion relations for macroscopic variables to describe the time development of the network. By taking the equilibrium limit of the recursion relations, we find that a certain type of nonmonotonic input-output relation of a single neuron yields an enhanced memory capacity of the network compared with the conventional monotonic relation. This behavior is in agreement with the prediction of Morita et al. who used numerical simulations as well as geometrical arguments to reach their conclusion. Our method reveals the relation between the type of the input-output function and the memory capacity in a generic associative memory.

KW - Amari-Maginu theory

KW - Associative memory

KW - Gaussian noise

KW - Input-output function

KW - Macrovariables

KW - Monte Carlo simulation

KW - Retrieval dynamics

KW - Signal-to-noise ratio analysis

UR - http://www.scopus.com/inward/record.url?scp=0027139024&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027139024&partnerID=8YFLogxK

U2 - 10.1016/S0893-6080(09)80017-8

DO - 10.1016/S0893-6080(09)80017-8

M3 - Article

AN - SCOPUS:0027139024

VL - 6

SP - 1061

EP - 1067

JO - Neural Networks

JF - Neural Networks

SN - 0893-6080

IS - 8

ER -