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Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model...
Human brain provides an accurate and energy efficient function for localizing sound sources. In order to build a localizing system with similar advantages, a biologically plausible spiking model for interaural level difference (ILD) processing auditory pathway from cochlea to the inferior colliclus in human brain is proposed in this paper. The biological plausibility is based on the facts that all...
We present a novel model to represent and match contour lines of closed shapes. This model is based on the mechanism of visual cortex. It extracts orientation features from input images with simple computation units that imitate simple cells in the visual cortex. The contour lines are accurately located by searching adjacent activated simple units. These activated simple units are concatenated in...
In this paper, the existence of periodic and partly periodic oscillation for a recurrent neural network with time-varying input and time delays between neural interconnections is investigated. Some theorems to determine the conditions for periodic oscillations are demonstrated. Simple and practical criteria for selecting the parameters in this network are derived. Typical simulation examples are also...
Divergent thinking refers to a style of thinking that ranges across a broad range of concepts, and is considered to be a core enabler of creativity. Thinking is often modeled as a process of conceptual combination, and creative ideas are seen as those using unconventional combinations of concepts. Since conceptual combination is fundamentally an associative process, it has been proposed that creative...
This paper introduces techniques for Deep Learning in conjunction with spiked random neural networks that closely resemble the stochastic behaviour of biological neurons in mammalian brains. The paper introduces clusters of such random neural networks and obtains the characteristics of their collective behaviour. Combining this model with previous work on extreme learning machines, we develop multilayer...
Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroen-cephalography (EEG) data collected from people affected by Alzheimer's Disease (AD) and people diagnosed with mild cognitive impairment (MCI). An evolving spatio-temporal data machine (eSTDM), named the NeuCube architecture, is used to analyse changes of neural activity across different brain regions...
Spiking neural networks are rapidly gaining popularity for their ability to perform efficient computation akin to the way a brain processes information. It has the potential to achieve low cost and high energy efficiency due to the distributed nature of neural computation and the use of low energy spikes for information exchange. A stochastic spiking neural network naturally can be used to realize...
In recent years, measuring the airflow in a fan system is a critical need in many industrial plants. This paper shows the development, implementation and analysis of an intelligent control method applicable to industrial systems. A performance comparison between two methods is shown: the conventional PI controller and Brain Emotional Learning Based Intelligent Controller (BELBIC). The control methods...
Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it interacts with its environment. Conventional model-based RL algorithms learn rapidly, but can be slow to adapt to sudden changes in the environment. Animals' brains, however, are thought to employ model-based RL mechanisms for learning, but are able to adapt to changes with relative ease. By employing “transfer...
Non-invasive EEG signal based brain computer interface (BCI) for motor imagery task - classification requires large number of subject specific training samples for each user session that reduces the user feasibility of BCI. A generalized classifier using few subject specific sample will ease the real world implementation of motor imagery based BCI. At first, this paper applies an improved active transfer...
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains unattained. Efforts to replicate such high-level brain functions on the basis of the limited, low-level known details of the brain have naturally led to critical...
In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine women participate in our driving simulation experiments. Two fusion strategies are adopted: feature level fusion (FLF) and decision level fusion (DLF). PERCLOS (the percentage of eye closure)...
Evolution is extremely creative. The mere availability of a mechanism for synaptic change seems to be enough for evolution to derive a learning rule. Many simulations of evolution have evolved learning in a highly guided manner. Either by constraining the update function to a Hebbian form, or by supplying an error/teaching signal. In this paper, we aim to evolve a more general learning rule. And since...
Since the world we live in consists of objects that can be experienced through multiple senses simultaneously, there are advantages offered by a multisensory learning paradigm. Though advances are being made in the fields of neuroscience, cognitive science and psychology regarding multisensory learning, there is a gap that exists between these advances and the ability of current computational models...
Spatiotemporal patterns of neural activity have increasingly come to be seen as important for encoding information in the nervous system, motivating the development of various neurocomputational models. In this paper, we present a simple recurrent neural network model motivated by the need to understand the basis of voluntary motor control. For a given individual, any specific voluntary movement is...
Connectivity analysis has become an essential tool for the evaluation of functional brain dynamics. The functional connectivity between different parts of the brain, or between different sensors, is assumed to provide key information for the discrimination of brain responses. In this study, we propose an estimation of effective cortical connectivity measures in frontal and parietal areas of human...
Many processes in nature display quasi-periodic behavior, including variable stars in distant galaxies and oscillations in brains. In this work we model quasi-periodic lightcurves using neuropercolation, which describes complex spatio-temporal oscillations arising from random cellular automata near criticality. We show that neuropercolation is able to model lightcurves from various stars of the gamma-Doradus...
The theory and experiments outlined in Weng 2015 [1] modeled brains as naturally emerging Turing Machines (TMs) inside Developmental Networks (DNs) — a new class of brain inspired neural networks. However, TMs originally proposed by Alan Turing 1936 [2] were deterministic. If they involved probability to handle uncertainty, the probability was in the mind of the human programmer for a specific task...
In previous work, several results on rapid real-time decisions from description were simulated using a neural network model including analogs of orbitofrontal cortex, amygdala, anterior cingulate, and striatum [1, 2]. This model marries adaptive resonance theory and fuzzy trace theory to develop categories that selectively weight numerical and qualitative attributes of linguistically presented options...
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