Cognitive clustering algorithm for efficient cybersecurity applications

Face recognition through a chaotic neural network model

“…Examples include the olfactory bulb, hippocampal regions, and the prepyriform cortex [20]. KII has four types of interactions: excitatory-excitatory, inhibitory-inhibitory, excitatoryinhibitory, and inhibitory-excitatory [14].…”

“…Transitions from point attractor to limit cycle attractor can be achieved through a suitable level of feedback gain or by input stimuli. Systematic analysis has identified regions of stability and transitions between limit cycle and fixed point dynamics [20].…”

“…The increase in nonlinear feedback gain is driven by the input results in a destabilization of background activity and leads to the emergence of a amplitude modulation (AM) pattern in KIII. KIII sets are responsible for the embodiment of meaning in AM patterns of neural activity shaped by synaptic interactions that have been modified through learning in their layers [20].…”

“…The K sets have been used to predict time series [15], character recognition [18], recognition of complex patterns [19], clustering [20], and face recognition [21], [22], [23].…”

Motor imagery classification for brain-computer interfaces through a chaotic neural network

“…The interaction between populations of inverse polarities makes the KII form several types of oscillators according with the configuration of its connections [12]. The type of attractor formed varies greatly from the weight parameters.…”