Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities

Kasabov, Nikola

doi:10.1007/978-3-540-71984-7_9

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…Our proposed SNN architecture functions as a spatiotemporal machine, employing a brain-inspired spiking neural network design. Its overarching objectives encompass knowledge extraction, STBD learning modeling, and investigation into the neurological mechanisms underpinning data generation [52], [53]. In our proposed approach, the 3D SNNr module is merged with an LSTM network, enhancing the comprehension and classification of depression.…”

Section: The Proposed Snn Architecture In Combination With An Lstm Ne...mentioning

confidence: 99%

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks

Sam,

Boostani,

Hashempour

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain's underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional braintemplate structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.

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Section: The Proposed Snn Architecture In Combination With An Lstm Ne...mentioning

confidence: 99%

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks

Sam,

Boostani,

Hashempour

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…12, the plant output tracks to each desired plant output using the learning capability of the QNN. Figures 14, 15, 16 and 17 show the responses to controlling the following non-linear plants: plant 1: Equation (15) in which the control input is added to the plant through the following saturation function:…”

Section: The Initial Values Ofmentioning

confidence: 99%

“…Subsequently, quantum theoretical concepts and techniques have influenced neural computing. On the basis of the analogy between quantum mechanics and neural networks, several quantum neural networks (QNNs) have been proposed [14][15][16][17][18]. QNNs can be classified as complex neural networks because the state of an arbitrary neuron in the QNN is a coherent superposition of multiple quantum states that can be expressed by complex numbers.…”

Section: Introductionmentioning

confidence: 99%

Self-tuning PID controller using quantum neural network with qubit-inspired neurons

Takahashi

Shiotani

Hashimoto

2015

HIS

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This paper investigates the performance of an adaptive controller using a multi-layer quantum neural network (QNN) comprising qubit-inspired neurons as information processing units. The control system is a self-tuning controller whose control parameters are tuned online by the QNN to track plant output relative to the desired plant output generated by a reference model. A proportional-integral-derivative (PID) controller is utilized as a conventional controller, with its parameters tuned by the QNN. Computational experiments to control a single-input single-output discrete-time non-linear plant are conducted to evaluate the capability and characteristics of the quantum neural self-tuning controller. The experiment results demonstrate the feasibility and effectiveness of the proposed controller.

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“…Incorporating probability into neural networks provides a suitable approach to capture this inherent feature of neural signal processing. In this context, several noteworthy studies have already demonstrated the advantages of integrating probability into third-generation neural networks, specifically spiking neural networks, enabling applications in classification, pattern recognition, and various other fields [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Bio-Inspired Probabilistic Neural Network Model for Noise-Resistant Collision Perception

Hong,

Sun,

Peng

et al. 2024

Biomimetics

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Bio-inspired models based on the lobula giant movement detector (LGMD) in the locust’s visual brain have received extensive attention and application for collision perception in various scenarios. These models offer advantages such as low power consumption and high computational efficiency in visual processing. However, current LGMD-based computational models, typically organized as four-layered neural networks, often encounter challenges related to noisy signals, particularly in complex dynamic environments. Biological studies have unveiled the intrinsic stochastic nature of synaptic transmission, which can aid neural computation in mitigating noise. In alignment with these biological findings, this paper introduces a probabilistic LGMD (Prob-LGMD) model that incorporates a probability into the synaptic connections between multiple layers, thereby capturing the uncertainty in signal transmission, interaction, and integration among neurons. Comparative testing of the proposed Prob-LGMD model and two conventional LGMD models was conducted using a range of visual stimuli, including indoor structured scenes and complex outdoor scenes, all subject to artificial noise. Additionally, the model’s performance was compared to standard engineering noise-filtering methods. The results clearly demonstrate that the proposed model outperforms all comparative methods, exhibiting a significant improvement in noise tolerance. This study showcases a straightforward yet effective approach to enhance collision perception in noisy environments.

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Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities

Cited by 9 publications

References 42 publications

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks

Self-tuning PID controller using quantum neural network with qubit-inspired neurons

A Bio-Inspired Probabilistic Neural Network Model for Noise-Resistant Collision Perception

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