### Abstract

We study a population decoding paradigm in which the maximum likelihood inference is based on an unfaithful decoding model (UMLI). This is usually the case for neural population decoding because the encoding process of the brain is not exactly known, or because a simplified decoding model is preferred for saving computational cost. We consider an unfaithful decoding model which neglects the pair-wise correlation between neuronal activities, and prove that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited-range. The performance of UMLI is compared with that of the maximum likelihood inference based on a faithful model and that of the center of mass decoding method. It turns out that UMLI has advantages of decreasing the computational complexity remarkablely and maintaining a high-level decoding accuracy at the same time. The effect of correlation on the decoding accuracy is also discussed.

Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999 |

Publisher | Neural information processing systems foundation |

Pages | 192-198 |

Number of pages | 7 |

ISBN (Print) | 0262194503, 9780262194501 |

Publication status | Published - 2000 Jan 1 |

Event | 13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States Duration: 1999 Nov 29 → 1999 Dec 4 |

### Publication series

Name | Advances in Neural Information Processing Systems |
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ISSN (Print) | 1049-5258 |

### Conference

Conference | 13th Annual Neural Information Processing Systems Conference, NIPS 1999 |
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Country | United States |

City | Denver, CO |

Period | 99/11/29 → 99/12/4 |

### ASJC Scopus subject areas

- Computer Networks and Communications
- Information Systems
- Signal Processing

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## Cite this

*Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999*(pp. 192-198). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.