AIMC Topic: Pattern Recognition, Physiological
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Repetitive training enhances the pattern recognition capability of cultured neural networks.
PLoS computational biology
Apr 22, 2025
Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neu...
A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments.
Nature communications
Jan 10, 2022
Object recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and c...
Proprioceptive Recognition with Artificial Neural Networks Based on Organizations of Spinocerebellar Tract and Cerebellum.
International journal of neural systems
Dec 9, 2018
Muscle kinematics and kinetics are nonlinearly encoded by proprioceptors, and the changes in muscle length and velocity are integrated into Ia afferent. Besides, proprioceptive signals from multiple muscles are probably mixed in afferent pathways, wh...
Design of deep echo state networks.
Neural networks : the official journal of the International Neural Network Society
Aug 8, 2018
In this paper, we provide a novel approach to the architectural design of deep Recurrent Neural Networks using signal frequency analysis. In particular, focusing on the Reservoir Computing framework and inspired by the principles related to the inher...
Music viewed by its entropy content: A novel window for comparative analysis.
PloS one
Oct 17, 2017
Polyphonic music files were analyzed using the set of symbols that produced the Minimal Entropy Description, which we call the Fundamental Scale. This allowed us to create a novel space to represent music pieces by developing: (a) a method to adjust ...
Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach.
Brain and behavior
Apr 26, 2017
INTRODUCTION: Scalp-recorded electrophysiological responses to complex, periodic auditory signals reflect phase-locked activity from neural ensembles within the auditory system. These responses, referred to as frequency-following responses (FFRs), ha...
Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.
PLoS computational biology
Oct 19, 2016
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combine...
Evolving Spiking Neural Networks for Recognition of Aged Voices.
Journal of voice : official journal of the Voice Foundation
Apr 1, 2016
The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. This is a relevant problem to those people who use their voices professionally, and its early identification can help ...
Middle Aged
Adolescent
Age Factors
Sound Spectrography
Speech Acoustics
Signal Processing, Computer-Assisted
Humans
Acoustics
Algorithms
Neural Networks, Computer
Adult
Female
Male
Aging
Aged
Speech Production Measurement
Aged, 80 and over
Pattern Recognition, Physiological
Young Adult
Voice Quality
Machine Learning Capabilities of a Simulated Cerebellum.
IEEE transactions on neural networks and learning systems
Jan 26, 2016
This paper describes the learning and control capabilities of a biologically constrained bottom-up model of the mammalian cerebellum. Results are presented from six tasks: 1) eyelid conditioning; 2) pendulum balancing; 3) proportional-integral-deriva...
A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.
IEEE transactions on neural networks and learning systems
Jan 27, 2015
This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike...
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