A new cognitive filtering approach based on Freeman K3 Neural Networks

Rosa, João Luís Garcia; Piazentin, Denis

doi:10.1007/s10489-016-0772-4

Cited by 6 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The original mathematical formulation of the model was based on a set of second-order ordinary differential equations (ODEs) with distributed parameters (for an overview, see Kozma and Freeman, 2001). Freeman K sets have been used in the past decades for pattern recognition, time series prediction, autonomous navigation and control, and clustering in cyber-security domains (Harter and Kozma, 2005; Kozma et al, 2007; Freeman and Kozma, 2010; Rosa and Piazentin, 2016). …”

Section: Discussion On Graph Theory Interpretation Of Cortical Phase mentioning

confidence: 99%

Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems

Kozma

Freeman

2017

Front. Syst. Neurosci.

View full text Add to dashboard Cite

Measurements of local field potentials over the cortical surface and the scalp of animals and human subjects reveal intermittent bursts of beta and gamma oscillations. During the bursts, narrow-band metastable amplitude modulation (AM) patters emerge for a fraction of a second and ultimately dissolve to the broad-band random background activity. The burst process depends on previously learnt conditioned stimuli (CS), thus different AM patterns may emerge in response to different CS. This observation leads to our cinematic theory of cognition when perception happens in discrete steps manifested in the sequence of AM patterns. Our article summarizes findings in the past decades on experimental evidence of cinematic theory of cognition and relevant mathematical models. We treat cortices as dissipative systems that self-organize themselves near a critical level of activity that is a non-equilibrium metastable state. Criticality is arguably a key aspect of brains in their rapid adaptation, reconfiguration, high storage capacity, and sensitive response to external stimuli. Self-organized criticality (SOC) became an important concept to describe neural systems. We argue that transitions from one AM pattern to the other require the concept of phase transitions, extending beyond the dynamics described by SOC. We employ random graph theory (RGT) and percolation dynamics as fundamental mathematical approaches to model fluctuations in the cortical tissue. Our results indicate that perceptions are formed through a phase transition from a disorganized (high entropy) to a well-organized (low entropy) state, which explains the swiftness of the emergence of the perceptual experience in response to learned stimuli.

show abstract

Section: Discussion On Graph Theory Interpretation Of Cortical Phase mentioning

confidence: 99%

Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems

Kozma

Freeman

2017

Front. Syst. Neurosci.

View full text Add to dashboard Cite

show abstract

“…As a bionic model, KIII also shows a certain pattern recognition ability in its application. At present, it has been applied to tea classification [6], face recognition [7,8], fabric tissue recognition [9], and preprocessing filters [10], these research results provide strong support for the application of the KIII model in different fields, further demonstrating its potential in pattern recognition and data processing.…”

Section: Introductionmentioning

confidence: 89%

Improving KIII model and its application intraffic sign recognition

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

The KIII model is a bionic olfactory model proposed based on the physiological structure of the animal olfactory system and has been applied to pattern recognition problems such as tea classification and EEG recognition. To explore the versatility of the KIII model and improve its performance of the KIII model, this study improved the KIII model by introducing adaptive histogram equalization, Gaussian filtering, and feature fusion methods based on gridded feature extraction and cropping in the preprocessing stage, the effective extraction of image features enhances the model recognition ability; Aiming at the computational cost of solving the dynamic equations of neurons in the KIII model, the fourth-order Runge-Kutta and Euler methods are weighed, and the results show that the Euler method has a relatively low cost in terms of time overhead. Low cost and good recognition results; compared several different measurement algorithms in the decision-making stage, according to the analysis, the Spearman correlation coefficient performed best in this scenario; and was used in the new field of traffic sign recognition, The experimental results show that the improved KIII model has achieved an average recognition effect of 95.18% on the TSRD dataset.

show abstract

“…Additionally, inhibitory 'B' and excitatory 'A' cells form the KII model, corresponding to the prepyriform cortex (PC). 'C' represents deep pyramidal cells [6,[13][14][15].…”

Section: The Kiii Modelmentioning

confidence: 99%

“…The KIII model not only exhibits a high degree of biomimicry but has also shown good pattern recognition capabilities in applications. It has already been utilized in fields like tea leaf categorization [10], facial recognition [11,12], fabric texture identification [13], pre-processing filters [14], and electroencephalogram (EEG) recognition [15], showcasing its potential in pattern recognition and data processing. Although the KIII model excels in certain areas, there remain some issues worthy of attention: lengthy training times, suboptimal feature extraction abilities, and an overly simplistic decision-making approach.…”

Section: Introductionmentioning

confidence: 99%

An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition

Zhang,

He,

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In human and other organisms’ perception, olfaction plays a vital role, and biomimetic olfaction models offer a pathway for studying olfaction. The most optimal existing biomimetic olfaction model is the KIII model proposed by Professor Freeman; however, it still exhibits certain limitations. This study aims to address these limitations: In the feature extraction stage, it introduces adaptive histogram equalization, Gaussian filtering, and discrete cosine transform methods, effectively enhancing and extracting high-quality image features, thereby bolstering the model’s recognition capabilities. To tackle the computational cost issue associated with solving the numerical solutions of neuronal dynamics equations in the KIII model, it replaces the original method with the faster Euler method, reducing time expenses while maintaining good recognition results. In the decision-making stage, several different dissimilarity metrics are compared, and the results indicate that the Spearman correlation coefficient performs best in this context. The improved KIII model is applied to a new domain of traffic sign recognition, demonstrating that it outperforms the baseline KIII model and exhibits certain advantages compared to other models.

show abstract

A new cognitive filtering approach based on Freeman K3 Neural Networks

Cited by 6 publications

References 43 publications

Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems

Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems

Improving KIII model and its application intraffic sign recognition

An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition

Contact Info

Product

Resources

About