Results 321 to 330 of about 2,000,131 (360)
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Biologically motivated quantum neural networks
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017This paper presents one step toward creating the building blocks for machine intelligence that is inspired by its biological equivalent. The authors' quantum learning methods (deep quantum learning) are applied to quantum devices whose quantum bit (q-bit) activity is deliberately chosen to mimic the spiking behavior of biological neurons.
James E. Steck, Elizabeth C. Behrman
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IEEE Transactions on Neural Networks and Learning Systems, 2020
Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons.
Shuangming Yang +7 more
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Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons.
Shuangming Yang +7 more
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Memristor-Based Design of Sparse Compact Convolutional Neural Network
IEEE Transactions on Network Science and Engineering, 2020Memristor has been widely studied for hardware implementation of neural networks due to the advantages of nanometer size, low power consumption, fast switching speed and functional similarity to biological synapse.
Shiping Wen +6 more
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2009
This chapter contains sections titled: Neuron Physiology Neuronal Diversity Specifications of the Brain The Eye's Neural Network Areas for Further Investigation Review Questions ...
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This chapter contains sections titled: Neuron Physiology Neuronal Diversity Specifications of the Brain The Eye's Neural Network Areas for Further Investigation Review Questions ...
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Neural networks in biological taxonomy
Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 2005The need for constant improvements in the classification of living organisms brings to biology an increased need for mathematical and computational tools. This need is being addressed at the Departments of Computer Sciences and Biochemistry, Universidade Federal de Minas Gerais, Brazil, by the development of neural network programs directed towards the
A.L. de Senna +3 more
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2018
“Is there intelligent life in outer space?” OK, that may be a little bit tongue in cheek, but, think about it, maybe it is a valid question. How much biological intelligence is there on earth? Where does it come from? And how much greater can it be? Is it just a matter of “bigger brains” or more complex neural networks inside our skulls?
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“Is there intelligent life in outer space?” OK, that may be a little bit tongue in cheek, but, think about it, maybe it is a valid question. How much biological intelligence is there on earth? Where does it come from? And how much greater can it be? Is it just a matter of “bigger brains” or more complex neural networks inside our skulls?
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A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network
Biologically Inspired Cognitive Architectures, 2018Abstract In this paper, a brain-inspired fuzzy emotional neural network (FUZZ-ENN) is proposed for uncertainty prediction tasks in real world applications. In the proposed FUZZ-ENN, amygdala connections are modeled by fuzzy IF-THEN behavioral rules and orbitofrontal module inhibits the amygdala responses in order to decrease the uncertainty.
Ehsan Zamirpour, Mohammad Mosleh
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Optimal filtering in biological neural networks
Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001Understanding how a population of biological neurons encode and decode signals, is a primary task in biological control problems. This enables one to understand how the sensory organs detect and process a signal which finally results in generating a motor command.
A.D. Polpitiya, Z. Nenadic, B.K. Ghosh
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Biologically Inspired Models to Train Neural Networks
Neural Computing & Applications, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Overcoming catastrophic forgetting in neural networks
arXiv.orgCatastrophic forgetting is the primary challenge that hinders continual learning, which refers to a neural network ability to sequentially learn multiple tasks while retaining previously acquired knowledge.
Brandon Shuen Yi Loke +4 more
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