AMI: An Auditory Machine Intelligence Algorithm for Predicting Sensory-Like Data

Emmanuel Ndidi OSEGİ; Vincent ANİREH

Makale detayları

AMI: An Auditory Machine Intelligence Algorithm for Predicting Sensory-Like Data

Yazarlar:

Emmanuel Ndidi OSEGİ | Vincent ANİREH

Yayıncı:

Ali KARCI

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

Computer Science 2020 Vol.5 Issue 2, pp.71-89

Belge Tipi:

Makale

Yayın Tarihi:

11/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000895896

Sıklık:

İki Yıllık

eISSN:

2548-1304

pISSN:

2548-1304

Anahtar kelimeler:

Özet:

 

In this paper, we present the results of our experiments using a new biologically constrained machine intelligence algorithm based on neural processing in the auditory cortex called auditory machine intelligence (AMI). This algorithm is an online learning technique for predicting sensory time series data i.e. data that comes in streams or a sequential order. The AMI algorithm is particularly inspired by the mismatch negativity effect which provides important evidence that the brain learns a statistical structure of the world it senses. We show through a number of experiments with popular benchmarks, how this algorithm may be applied in a real world sense. The results of these experiments have also been compared with two very popular techniques that have been used for time series predictions and are very encouraging.

 

In this paper, we present the results of our experiments using a new biologically constrained machine intelligence algorithm based on neural processing in the auditory cortex called auditory machine intelligence (AMI). This algorithm is an online learning technique for predicting sensory time series data i.e. data that comes in streams or a sequential order. The AMI algorithm is particularly inspired by the mismatch negativity effect which provides important evidence that the brain learns a statistical structure of the world it senses. We show through a number of experiments with popular benchmarks, how this algorithm may be applied in a real world sense. The results of these experiments have also been compared with two very popular techniques that have been used for time series predictions and are very encouraging.

Makale detayları

AMI: An Auditory Machine Intelligence Algorithm for Predicting Sensory-Like Data

Yazarlar:

Emmanuel Ndidi OSEGİ | Vincent ANİREH

Yayıncı:

Ali KARCI

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

Computer Science 2020 Vol.5 Issue 2, pp.71-89

Belge Tipi:

Makale

Yayın Tarihi:

11/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000895896

Sıklık:

İki Yıllık

eISSN:

2548-1304

pISSN:

2548-1304

Auditory processing

biological machine intelligence

predictions

sensory data

time series

Sayı

Makale detayları

AMI: An Auditory Machine Intelligence Algorithm for Predicting Sensory-Like Data

Yazarlar: Emmanuel Ndidi OSEGİ | Vincent ANİREH

Yayıncı: Ali KARCI

Ana Başlıklar: Veri işleme & bilgisayar bilimi

Dergi: Computer Science 2020 Vol.5 Issue 2, pp.71-89

Belge Tipi: Makale

Yayın Tarihi: 11/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000895896

Sıklık: İki Yıllık

eISSN: 2548-1304

pISSN: 2548-1304

Auditory processing

biological machine intelligence

predictions

sensory data

time series

Sayı

Yazarlar:

Emmanuel Ndidi OSEGİ | Vincent ANİREH

Yayıncı:

Ali KARCI

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

Computer Science 2020 Vol.5 Issue 2, pp.71-89

Belge Tipi:

Makale

Yayın Tarihi:

11/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000895896

Sıklık:

İki Yıllık

eISSN:

2548-1304

pISSN:

2548-1304